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Understanding Clinical Trials

Clinical research: what is it.

Your doctor may have said that you are eligible for a clinical trial, or you may have seen an ad for a clinical research study. What is clinical research, and is it right for you?

Clinical research is the comprehensive study of the safety and effectiveness of the most promising advances in patient care. Clinical research is different than laboratory research. It involves people who volunteer to help us better understand medicine and health. Lab research generally does not involve people — although it helps us learn which new ideas may help people.

Every drug, device, tool, diagnostic test, technique and technology used in medicine today was once tested in volunteers who took part in clinical research studies.

At Johns Hopkins Medicine, we believe that clinical research is key to improve care for people in our community and around the world. Once you understand more about clinical research, you may appreciate why it’s important to participate — for yourself and the community.

What Are the Types of Clinical Research?

There are two main kinds of clinical research:

Observational Studies

Observational studies are studies that aim to identify and analyze patterns in medical data or in biological samples, such as tissue or blood provided by study participants.

Clinical Trials

Clinical trials, which are also called interventional studies, test the safety and effectiveness of medical interventions — such as medications, procedures and tools — in living people.

Clinical research studies need people of every age, health status, race, gender, ethnicity and cultural background to participate. This will increase the chances that scientists and clinicians will develop treatments and procedures that are likely to be safe and work well in all people. Potential volunteers are carefully screened to ensure that they meet all of the requirements for any study before they begin. Most of the reasons people are not included in studies is because of concerns about safety.

Both healthy people and those with diagnosed medical conditions can take part in clinical research. Participation is always completely voluntary, and participants can leave a study at any time for any reason.

“The only way medical advancements can be made is if people volunteer to participate in clinical research. The research participant is just as necessary as the researcher in this partnership to advance health care.” Liz Martinez, Johns Hopkins Medicine Research Participant Advocate

Types of Research Studies

Within the two main kinds of clinical research, there are many types of studies. They vary based on the study goals, participants and other factors.

Biospecimen studies

Healthy volunteer studies.

Clinical trials study the safety and effectiveness of interventions and procedures on people’s health. Interventions may include medications, radiation, foods or behaviors, such as exercise. Usually, the treatments in clinical trials are studied in a laboratory and sometimes in animals before they are studied in humans. The goal of clinical trials is to find new and better ways of preventing, diagnosing and treating disease. They are used to test:

Drugs or medicines

New types of surgery

Medical devices

New ways of using current treatments

New ways of changing health behaviors

New ways to improve quality of life for sick patients

 Goals of Clinical Trials

Because every clinical trial is designed to answer one or more medical questions, different trials have different goals. Those goals include:

Treatment trials

Prevention trials, screening trials, phases of a clinical trial.

In general, a new drug needs to go through a series of four types of clinical trials. This helps researchers show that the medication is safe and effective. As a study moves through each phase, researchers learn more about a medication, including its risks and benefits.

Is the medication safe and what is the right dose?   Phase one trials involve small numbers of participants, often normal volunteers.

Does the new medication work and what are the side effects?   Phase two trials test the treatment or procedure on a larger number of participants. These participants usually have the condition or disease that the treatment is intended to remedy.

Is the new medication more effective than existing treatments?  Phase three trials have even more people enrolled. Some may get a placebo (a substance that has no medical effect) or an already approved treatment, so that the new medication can be compared to that treatment.

Is the new medication effective and safe over the long term?   Phase four happens after the treatment or procedure has been approved. Information about patients who are receiving the treatment is gathered and studied to see if any new information is seen when given to a large number of patients.

“Johns Hopkins has a comprehensive system overseeing research that is audited by the FDA and the Association for Accreditation of Human Research Protection Programs to make certain all research participants voluntarily agreed to join a study and their safety was maximized.” Gail Daumit, M.D., M.H.S., Vice Dean for Clinical Investigation, Johns Hopkins University School of Medicine

Is It Safe to Participate in Clinical Research?

There are several steps in place to protect volunteers who take part in clinical research studies. Clinical Research is regulated by the federal government. In addition, the institutional review board (IRB) and Human Subjects Research Protection Program at each study location have many safeguards built in to each study to protect the safety and privacy of participants.

Clinical researchers are required by law to follow the safety rules outlined by each study's protocol. A protocol is a detailed plan of what researchers will do in during the study.

In the U.S., every study site's IRB — which is made up of both medical experts and members of the general public — must approve all clinical research. IRB members also review plans for all clinical studies. And, they make sure that research participants are protected from as much risk as possible.

Earning Your Trust

This was not always the case. Many people of color are wary of joining clinical research because of previous poor treatment of underrepresented minorities throughout the U.S. This includes medical research performed on enslaved people without their consent, or not giving treatment to Black men who participated in the Tuskegee Study of Untreated Syphilis in the Negro Male. Since the 1970s, numerous regulations have been in place to protect the rights of study participants.

Many clinical research studies are also supervised by a data and safety monitoring committee. This is a group made up of experts in the area being studied. These biomedical professionals regularly monitor clinical studies as they progress. If they discover or suspect any problems with a study, they immediately stop the trial. In addition, Johns Hopkins Medicine’s Research Participant Advocacy Group focuses on improving the experience of people who participate in clinical research.

Clinical research participants with concerns about anything related to the study they are taking part in should contact Johns Hopkins Medicine’s IRB or our Research Participant Advocacy Group .

Learn More About Clinical Research at Johns Hopkins Medicine

For information about clinical trial opportunities at Johns Hopkins Medicine, visit our trials site.

Video Clinical Research for a Healthier Tomorrow: A Family Shares Their Story

Clinical Research for a Healthier Tomorrow: A Family Shares Their Story

Research Types Explained: Basic, Clinical, Translational

“Research” is a broad stroke of a word, the verbal equivalent of painting a wall instead of a masterpiece. There are important distinctions among the three principal types of medical research — basic, clinical and translational.

Whereas basic research is looking at questions related to how nature works, translational research aims to take what’s learned in basic research and apply that in the development of solutions to medical problems. Clinical research, then, takes those solutions and studies them in clinical trials. Together, they form a continuous research loop that transforms ideas into action in the form of new treatments and tests, and advances cutting-edge developments from the lab bench to the patient’s bedside and back again.

Basic Research

When it comes to science, the “basic” in basic research describes something that’s an essential starting point. “If you think of it in terms of construction, you can’t put up a beautiful, elegant house without first putting in a foundation,” says David Frank, MD , Associate Professor of Medicine, Medical Oncology, at Dana-Farber Cancer Institute. “In science, if you don’t first understand the basic research, then you can’t move on to advanced applications.”

Basic medical research is usually conducted by scientists with a PhD in such fields as biology and chemistry, among many others. They study the core building blocks of life — DNA, cells, proteins, molecules, etc. — to answer fundamental questions about their structures and how they work.

For example, oncologists now know that mutations in DNA enable the unchecked growth of cells in cancer. A scientist conducting basic research might ask: How does DNA work in a healthy cell? How do mutations occur? Where along the DNA sequence do mutations happen? And why?

“Basic research is fundamentally curiosity-driven research,” says Milka Kostic, Program Director, Chemical Biology at Dana-Farber Cancer Institute. “Think of that moment when an apple fell on Isaac Newton’s head. He thought to himself, ‘Why did that happen?’ and then went on to try to find the answer. That’s basic research.”

Clinical Research

Clinical research explores whether new treatments, medications and diagnostic techniques are safe and effective in patients. Physicians administer these to patients in rigorously controlled clinical trials, so that they can accurately and precisely monitor patients’ progress and evaluate the treatment’s efficacy, or measurable benefit.

“In clinical research, we’re trying to define the best treatment for a patient with a given condition,” Frank says. “We’re asking such questions as: Will this new treatment extend the life of a patient with a given type of cancer? Could this supportive medication diminish nausea, diarrhea or other side effects? Could this diagnostic test help physicians detect cancer earlier or distinguish between fast- and slow-growing cancers?”

Successful clinical researchers must draw on not only their medical training but also their knowledge of such areas as statistics, controls and regulatory compliance.

Translational Research

It’s neither practical nor safe to transition directly from studying individual cells to testing on patients. Translational research provides that crucial pivot point. It bridges the gap between basic and clinical research by bringing together a number of specialists to refine and advance the application of a discovery. “Biomedical science is so complex, and there’s so much knowledge available.” Frank says. “It’s through collaboration that advances are made.”

For example, let’s say a basic researcher has identified a gene that looks like a promising candidate for targeted therapy. Translational researchers would then evaluate thousands, if not millions, of potential compounds for the ideal combination that could be developed into a medicine to achieve the desired effect. They’d refine and test the compound on intermediate models, in laboratory and animal models. Then they would analyze those test results to determine proper dosage, side effects and other safety considerations before moving to first-in-human clinical trials. It’s the complex interplay of chemistry, biology, oncology, biostatistics, genomics, pharmacology and other specialties that makes such a translational study a success.

Collaboration and technology have been the twin drivers of recent quantum leaps in the quality and quantity of translational research. “Now, using modern molecular techniques,” Frank says, “we can learn so much from a tissue sample from a patient that we couldn’t before.”

Translational research provides a crucial pivot point after clinical trials as well. Investigators explore how the trial’s resulting treatment or guidelines can be implemented by physicians in their practice. And the clinical outcomes might also motivate basic researchers to reevaluate their original assumptions.

“Translational research is a two-way street,” Kostic says. “There is always conversation flowing in both directions. It’s a loop, a continuous cycle, with one research result inspiring another.”

Learn more about research at Dana-Farber .

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Medical research involves research in a wide range of fields, such as biology, chemistry, pharmacology and toxicology with the goal of developing new medicines or medical procedures or improving the application of those already available. It can be viewed as encompassing preclinical research (for example, in cellular systems and animal models) and clinical research (for example, clinical trials).

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Participating in Health Research Studies

What is health research.

• Is Health Research Safe?
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• Types of Health Research

The term "health research," sometimes also called "medical research" or "clinical research," refers to research that is done to learn more about human health. Health research also aims to find better ways to prevent and treat disease. Health research is an important way to help improve the care and treatment of people worldwide.

Have you ever wondered how certain drugs can cure or help treat illness? For instance, you might have wondered how aspirin helps reduce pain. Well, health research begins with questions that have not been answered yet such as:

"Does a certain drug improve health?"

To gain more knowledge about illness and how the human body and mind work, volunteers can help researchers answer questions about health in studies of an illness. Studies might involve testing new drugs, vaccines, surgical procedures, or medical devices in clinical trials . For this reason, health research can involve known and unknown risks. To answer questions correctly, safely, and according to the best methods, researchers have detailed plans for the research and procedures that are part of any study. These procedures are called "protocols."

An example of a research protocol includes the process for determining participation in a study. A person might meet certain conditions, called "inclusion criteria," if they have the required characteristics for a study. A study on menopause may require participants to be female. On the other hand, a person might not be able to enroll in a study if they do not meet these criteria based on "exclusion criteria." A male may not be able to enroll in a study on menopause. These criteria are part of all research protocols. Study requirements are listed in the description of the study.

A Brief History

While a few studies of disease were done using a scientific approach as far back as the 14th Century, the era of modern health research started after World War II with early studies of antibiotics. Since then, health research and clinical trials have been essential for the development of more than 1,000 Food and Drug Administration (FDA) approved drugs. These drugs help treat infections, manage long term or chronic illness, and prolong the life of patients with cancer and HIV.

Sound research demands a clear consent process. Public knowledge of the potential abuses of medical research arose after the severe misconduct of research in Germany during World War II. This resulted in rules to ensure that volunteers freely agree, or give "consent," to any study they are involved in. To give consent, one should have clear knowledge about the study process explained by study staff. Additional safeguards for volunteers were also written in the Nuremberg Code and the Declaration of Helsinki .

New rules and regulations to protect research volunteers and to eliminate ethical violations have also been put in to place after the Tuskegee trial . In this unfortunate study, African American patients with syphilis were denied known treatment so that researchers could study the history of the illness. With these added protections, health research has brought new drugs and treatments to patients worldwide. Thus, health research has found cures to many diseases and helped manage many others.

Why is Health Research Important?

The development of new medical treatments and cures would not happen without health research and the active role of research volunteers. Behind every discovery of a new medicine and treatment are thousands of people who were involved in health research. Thanks to the advances in medical care and public health, we now live on average 10 years longer than in the 1960's and 20 years longer than in the 1930's. Without research, many diseases that can now be treated would cripple people or result in early death. New drugs, new ways to treat old and new illnesses, and new ways to prevent diseases in people at risk of developing them, can only result from health research.

Before health research was a part of health care, doctors would choose medical treatments based on their best guesses, and they were often wrong. Now, health research takes the guesswork out. In fact, the Food and Drug Administration (FDA) requires that all new medicines are fully tested before doctors can prescribe them. Many things that we now take for granted are the result of medical studies that have been done in the past. For instance, blood pressure pills, vaccines to prevent infectious diseases, transplant surgery, and chemotherapy are all the result of research.

Medical research often seems much like standard medical care, but it has a distinct goal. Medical care is the way that your doctors treat your illness or injury. Its only purpose is to make you feel better and you receive direct benefits. On the other hand, medical research studies are done to learn about and to improve current treatments. We all benefit from the new knowledge that is gained in the form of new drugs, vaccines, medical devices (such as pacemakers) and surgeries. However, it is crucial to know that volunteers do not always receive any direct benefits from being in a study. It is not known if the treatment or drug being studied is better, the same, or even worse than what is now used. If this was known, there would be no need for any medical studies.

• Next: Is Health Research Safe? >>
• Last Updated: May 27, 2020 3:05 PM
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WHAT IS BIOMEDICAL RESEARCH?

Biomedical research is the broad area of science that looks for ways to prevent and treat diseases that cause illness and death in people and in animals. This general field of research includes many areas of both the life and physical sciences.

Utilizing biotechnology techniques, biomedical researchers study biological processes and diseases with the ultimate goal of developing effective treatments and cures. Biomedical research is an evolutionary process requiring careful experimentation by many scientists, including biologists and chemists. Discovery of new medicines and therapies requires careful scientific experimentation, development, and evaluation.

Why are Animals Used in Biomedical Research?

The use of animals in some types of research is essential to the development of new and more effective methods for diagnosing and treating diseases that affect both humans and animals. Scientists use animals to learn more about health problems, and to assure the safety of new medical treatments. Medical researchers need to understand health problems before they can develop ways to treat them. Some diseases and health problems involve processes that can only be studied in living organisms. Animals are necessary to medical research because it is impractical or unethical to use humans.

Animals make good research subjects for a variety of reasons. Animals are biologically similar to humans. They are susceptible to many of the same health problems, and they have short life-cycles so they can easily be studied throughout their whole life-span or across several generations. In addition, scientists can easily control the environment around animals (diet, temperature, lighting), which would be difficult to do with people. Finally, a primary reason why animals are used is that most people feel it would be wrong to deliberately expose human beings to health risks in order to observe the course of a disease.

Animals are used in research to develop drugs and medical procedures to treat diseases. Scientists may discover such drugs and procedures using alternative research methods that do not involve animals. If the new therapy seems promising, it is tested in animals to see whether it seems to be safe and effective. If the results of the animal studies are good, then human volunteers are asked to participate in a clinical trial. The animal studies are conducted first to give medical researchers a better idea of what benefits and complications they are likely to see in humans.

A variety of animals provide very useful models for the study of diseases afflicting both animals and humans. However, approximately 95 percent of research animals in the United States are rats, mice, and other rodents bred specifically for laboratory research. Dogs, cats, and primates account for less than one percent of all the animals used in research.

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Scientists continue to look for ways to reduce the numbers of animals needed to obtain valid results, refine experimental techniques, and replace animals with other research methods whenever feasible.

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What is Clinical Research?

Clinical research is the study of health and illness in people. It is the way we learn how to prevent, diagnose and treat illness. Clinical research describes many different elements of scientific investigation. Simply put, it involves human participants and helps translate basic research (done in labs) into new treatments and information to benefit patients. Clinical trials as well as research in epidemiology, physiology and pathophysiology, health services, education, outcomes and mental health can all fall under the clinical research umbrella.

Clinical Trials

A clinical trial is a type of clinical research study. A clinical trial is an experiment designed to answer specific questions about possible new treatments or new ways of using existing (known) treatments. Clinical trials are done to determine whether new drugs or treatments are safe and effective. Clinical trials are part of a long, careful process which may take many years to complete. First, doctors study a new treatment in the lab. Then they often study the treatment in animals. If a new treatment shows promise, doctors then test the treatment in people via a clinical trial.

Clinical Research vs. Medical Care

People often confuse a clinical research or clinical trials with medical care. This topic can be especially confusing if your doctor is also the researcher.  When you receive medical care from your own doctor, he or she develops a plan of care just for you. When you take part in a clinical research study, you and the researcher must follow a set plan called the “study protocol.” The researcher usually can’t adjust the plan for you – but the plan includes steps to follow if you aren’t doing well. It’s important to understand that a clinical trial is an experiment. By its nature, that means the answer to the research question is still unknown. You might or might not benefit directly by participating in a clinical research study. It is important to talk about this topic with your doctor/the researcher.

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Why Is Medical Research Important?

Medical research has become an important part of the health care industry, and advances in technology have made it possible for much of it to be done on an outpatient basis, meaning that investigators sometimes don’t need to do extensive studies in a research facility. The field of medical research is one of the most interesting fields in all of science because of its focus on science and medicine in conjunction with a great deal of research.

Medical research covers a wide variety of studies, stretching from ‘baseline’ investigation, through systematic reviews and to the cutting-edge of medical science. It involves the study of all human diseases or may have only disease-specific research. For example, AIDS research includes both studying patients who have AIDS and those who do not, as well as studying children with AIDS and children without AIDS. Similarly, researchers may be investigating the causes of Parkinson’s disease in old age and Parkinson’s disease in young adulthood.

4 Phases of Medical Research Studies

The four phases of Medical Research Studies are experimental, comparative/expository, understudy, and last, analysis and validation/regression.

• Comparative/expository medical research compares experimental and comparative samples from which the study population is developed; compare post hoc comparisons with the initial data; evaluates associations among variables measured.  
• Understudy studies consist of data from observational studies and random chance sampling.
• Experimental refers to clinical trials that are done specifically to test a new medical product, device, or technique.  
• Finally, validation/ regression Research studies compare new designs or drugs to earlier designs and evaluate their effect on any association found.

There are many reasons why medical research is so valuable. Whether you aim to start a career in this field or to gain more knowledge about health conditions and their treatments, it’s important to understand the benefits that medical research provides. You can learn about medical research at http://hrmdresearch.com/ .

The Importance Of Medical Research

The breakthroughs that people enjoy today are virtually unimaginable without the knowledge gained through medical research. Here are some of the most important reasons medical research is important:

• Generate Valuable Insights

Medical research helps people learn more about themselves and their health. The knowledge gained by medical research is constantly improving.

• With new scientific information coming from medical studies, people will be able to take care of their health and well-being more effectively.
• It also seeks to understand the reasons for diseases, to discover new methods of preventing or controlling diseases, and to develop treatments for these diseases and their effects.

2. Development Of New Drugs

Medical research must be done to find a cure for diseases and illnesses. Without medical research, medicine and other medical innovations as we know it could not exist. Sometimes called pharmaceutical research, medical research encompasses a broad spectrum of scientific studies. It starts with the research and development of drugs, followed by treatments and procedures used in clinical practice.  

The process of new drug development may involve the following steps:

• It can be done in several different ways, including doing laboratory experiments in bioresources such as blood and cells, or cell culture, to studying the effects of chronic exposure to toxins, drugs, hormones, and other compounds in the environment.  
• Other research is directed toward understanding disease mechanisms, to find better ways of treating or preventing disease, and how disease progression is influenced by environmental factors.  

3. Improve The Quality Of Life

Medical researchers don’t just look for ways to manage the symptoms of diseases, they try their best to find a cure for a specific illness or a group of diseases. There are several ways drug research and testing improve the quality of life:

• Medical studies that aid in the development and administration of vaccines allow people to live without worrying about deadly diseases. An example will be the ongoing development and clinical trials for the COVID-19 vaccine which may help the world go back to normal.
• Some people can live with their conditions for years by understanding how to manage them properly. As time goes by, everyone is also learning how to prevent certain illnesses from occurring and even eliminate them.

Why is medical research so important? Medical research saves lives every day. Scientists and researchers work day and night to develop new treatments, drugs, and procedures. Without the help of dedicated scientists, doctors, and other medical professionals, the advances made would be slow and limited.  

When a patient participates in a trial, they must undergo several physical tests and provide some information about their lifestyle and diet. The findings and insights derived from these studies and trials are invaluable in coming up with treatments and cures for various health conditions.

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Nih clinical research trials and you.

The NIH Clinical Trials and You website is a resource for people who want to learn more about clinical trials. By expanding the below questions, you can read answers to common questions about taking part in a clinical trial. 

What are clinical trials and why do people participate?

Clinical research is medical research that involves people like you. When you volunteer to take part in clinical research, you help doctors and researchers learn more about disease and improve health care for people in the future. Clinical research includes all research that involves people.  Types of clinical research include:

• Epidemiology, which improves the understanding of a disease by studying patterns, causes, and effects of health and disease in specific groups.
• Behavioral, which improves the understanding of human behavior and how it relates to health and disease.
• Health services, which looks at how people access health care providers and health care services, how much care costs, and what happens to patients as a result of this care.
• Clinical trials, which evaluate the effects of an intervention on health outcomes.

What are clinical trials and why would I want to take part?

Clinical trials are part of clinical research and at the heart of all medical advances. Clinical trials look at new ways to prevent, detect, or treat disease. Clinical trials can study:

• New drugs or new combinations of drugs
• New ways of doing surgery
• New medical devices
• New ways to use existing treatments
• New ways to change behaviors to improve health
• New ways to improve the quality of life for people with acute or chronic illnesses.

The goal of clinical trials is to determine if these treatment, prevention, and behavior approaches are safe and effective. People take part in clinical trials for many reasons. Healthy volunteers say they take part to help others and to contribute to moving science forward. People with an illness or disease also take part to help others, but also to possibly receive the newest treatment and to have added (or extra) care and attention from the clinical trial staff. Clinical trials offer hope for many people and a chance to help researchers find better treatments for others in the future

Why is diversity and inclusion important in clinical trials?

People may experience the same disease differently. It’s essential that clinical trials include people with a variety of lived experiences and living conditions, as well as characteristics like race and ethnicity, age, sex, and sexual orientation, so that all communities benefit from scientific advances.

See Diversity & Inclusion in Clinical Trials for more information.

How does the research process work?

The idea for a clinical trial often starts in the lab. After researchers test new treatments or procedures in the lab and in animals, the most promising treatments are moved into clinical trials. As new treatments move through a series of steps called phases, more information is gained about the treatment, its risks, and its effectiveness.

What are clinical trial protocols?

Clinical trials follow a plan known as a protocol. The protocol is carefully designed to balance the potential benefits and risks to participants, and answer specific research questions. A protocol describes the following:

• The goal of the study
• Who is eligible to take part in the trial
• Protections against risks to participants
• Details about tests, procedures, and treatments
• How long the trial is expected to last
• What information will be gathered

A clinical trial is led by a principal investigator (PI). Members of the research team regularly monitor the participants’ health to determine the study’s safety and effectiveness.

What is an Institutional Review Board?

Most, but not all, clinical trials in the United States are approved and monitored by an Institutional Review Board (IRB) to ensure that the risks are reduced and are outweighed by potential benefits. IRBs are committees that are responsible for reviewing research in order to protect the rights and safety of people who take part in research, both before the research starts and as it proceeds. You should ask the sponsor or research coordinator whether the research you are thinking about joining was reviewed by an IRB.

What is a clinical trial sponsor?

Clinical trial sponsors may be people, institutions, companies, government agencies, or other organizations that are responsible for initiating, managing or financing the clinical trial, but do not conduct the research.

What is informed consent?

Informed consent is the process of providing you with key information about a research study before you decide whether to accept the offer to take part. The process of informed consent continues throughout the study. To help you decide whether to take part, members of the research team explain the details of the study. If you do not understand English, a translator or interpreter may be provided. The research team provides an informed consent document that includes details about the study, such as its purpose, how long it’s expected to last, tests or procedures that will be done as part of the research, and who to contact for further information. The informed consent document also explains risks and potential benefits. You can then decide whether to sign the document. Taking part in a clinical trial is voluntary and you can leave the study at any time.

What are the types of clinical trials?

There are different types of clinical trials.

• Prevention trials look for better ways to prevent a disease in people who have never had the disease or to prevent the disease from returning. Approaches may include medicines, vaccines, or lifestyle changes.
• Screening trials test new ways for detecting diseases or health conditions.
• Diagnostic trials study or compare tests or procedures for diagnosing a particular disease or condition.
• Treatment trials test new treatments, new combinations of drugs, or new approaches to surgery or radiation therapy.
• Behavioral trials evaluate or compare ways to promote behavioral changes designed to improve health.
• Quality of life trials (or supportive care trials) explore and measure ways to improve the comfort and quality of life of people with conditions or illnesses.

What are the phases of clinical trials?

Clinical trials are conducted in a series of steps called “phases.” Each phase has a different purpose and helps researchers answer different questions.

• Phase I trials : Researchers test a drug or treatment in a small group of people (20–80) for the first time. The purpose is to study the drug or treatment to learn about safety and identify side effects.
• Phase II trials : The new drug or treatment is given to a larger group of people (100–300) to determine its effectiveness and to further study its safety.
• Phase III trials : The new drug or treatment is given to large groups of people (1,000–3,000) to confirm its effectiveness, monitor side effects, compare it with standard or similar treatments, and collect information that will allow the new drug or treatment to be used safely.
• Phase IV trials : After a drug is approved by the FDA and made available to the public, researchers track its safety in the general population, seeking more information about a drug or treatment’s benefits, and optimal use.

What do the terms placebo, randomization, and blinded mean in clinical trials?

In clinical trials that compare a new product or therapy with another that already exists, researchers try to determine if the new one is as good, or better than, the existing one. In some studies, you may be assigned to receive a placebo (an inactive product that resembles the test product, but without its treatment value).

Comparing a new product with a placebo can be the fastest and most reliable way to show the new product’s effectiveness. However, placebos are not used if you would be put at risk — particularly in the study of treatments for serious illnesses — by not having effective therapy. You will be told if placebos are used in the study before entering a trial.

Randomization is the process by which treatments are assigned to participants by chance rather than by choice. This is done to avoid any bias in assigning volunteers to get one treatment or another. The effects of each treatment are compared at specific points during a trial. If one treatment is found superior, the trial is stopped so that the most volunteers receive the more beneficial treatment.  This video helps explain randomization for all clinical trials .

" Blinded " (or " masked ") studies are designed to prevent members of the research team and study participants from influencing the results. Blinding allows the collection of scientifically accurate data. In single-blind (" single-masked ") studies, you are not told what is being given, but the research team knows. In a double-blind study, neither you nor the research team are told what you are given; only the pharmacist knows. Members of the research team are not told which participants are receiving which treatment, in order to reduce bias. If medically necessary, however, it is always possible to find out which treatment you are receiving.

Who takes part in clinical trials?

Many different types of people take part in clinical trials. Some are healthy, while others may have illnesses. Research procedures with healthy volunteers are designed to develop new knowledge, not to provide direct benefit to those taking part. Healthy volunteers have always played an important role in research.

Healthy volunteers are needed for several reasons. When developing a new technique, such as a blood test or imaging device, healthy volunteers help define the limits of "normal." These volunteers are the baseline against which patient groups are compared and are often matched to patients on factors such as age, gender, or family relationship. They receive the same tests, procedures, or drugs the patient group receives. Researchers learn about the disease process by comparing the patient group to the healthy volunteers.

Factors like how much of your time is needed, discomfort you may feel, or risk involved depends on the trial. While some require minimal amounts of time and effort, other studies may require a major commitment of your time and effort, and may involve some discomfort. The research procedure(s) may also carry some risk. The informed consent process for healthy volunteers includes a detailed discussion of the study's procedures and tests and their risks.

A patient volunteer has a known health problem and takes part in research to better understand, diagnose, or treat that disease or condition. Research with a patient volunteer helps develop new knowledge. Depending on the stage of knowledge about the disease or condition, these procedures may or may not benefit the study participants.

Patients may volunteer for studies similar to those in which healthy volunteers take part. These studies involve drugs, devices, or treatments designed to prevent,or treat disease. Although these studies may provide direct benefit to patient volunteers, the main aim is to prove, by scientific means, the effects and limitations of the experimental treatment. Therefore, some patient groups may serve as a baseline for comparison by not taking the test drug, or by receiving test doses of the drug large enough only to show that it is present, but not at a level that can treat the condition.

Researchers follow clinical trials guidelines when deciding who can participate, in a study. These guidelines are called Inclusion/Exclusion Criteria . Factors that allow you to take part in a clinical trial are called "inclusion criteria." Those that exclude or prevent participation are "exclusion criteria." These criteria are based on factors such as age, gender, the type and stage of a disease, treatment history, and other medical conditions. Before joining a clinical trial, you must provide information that allows the research team to determine whether or not you can take part in the study safely. Some research studies seek participants with illnesses or conditions to be studied in the clinical trial, while others need healthy volunteers. Inclusion and exclusion criteria are not used to reject people personally. Instead, the criteria are used to identify appropriate participants and keep them safe, and to help ensure that researchers can find new information they need.

What do I need to know if I am thinking about taking part in a clinical trial?

Risks and potential benefits

Clinical trials may involve risk, as can routine medical care and the activities of daily living. When weighing the risks of research, you can think about these important factors:

• The possible harms that could result from taking part in the study
• The level of harm
• The chance of any harm occurring

Most clinical trials pose the risk of minor discomfort, which lasts only a short time. However, some study participants experience complications that require medical attention. In rare cases, participants have been seriously injured or have died of complications resulting from their participation in trials of experimental treatments. The specific risks associated with a research protocol are described in detail in the informed consent document, which participants are asked to consider and sign before participating in research. Also, a member of the research team will explain the study and answer any questions about the study. Before deciding to participate, carefully consider risks and possible benefits.

Potential benefits

Well-designed and well-executed clinical trials provide the best approach for you to:

• Help others by contributing to knowledge about new treatments or procedures.
• Gain access to new research treatments before they are widely available.
• Receive regular and careful medical attention from a research team that includes doctors and other health professionals.

Risks to taking part in clinical trials include the following:

• There may be unpleasant, serious, or even life-threatening effects of experimental treatment.
• The study may require more time and attention than standard treatment would, including visits to the study site, more blood tests, more procedures, hospital stays, or complex dosage schedules.

What questions should I ask if offered a clinical trial?

If you are thinking about taking part in a clinical trial, you should feel free to ask any questions or bring up any issues concerning the trial at any time. The following suggestions may give you some ideas as you think about your own questions.

• What is the purpose of the study?
• Why do researchers think the approach may be effective?
• Who will fund the study?
• Who has reviewed and approved the study?
• How are study results and safety of participants being monitored?
• How long will the study last?
• What will my responsibilities be if I take part?
• Who will tell me about the results of the study and how will I be informed?

Risks and possible benefits

• What are my possible short-term benefits?
• What are my possible long-term benefits?
• What are my short-term risks, and side effects?
• What are my long-term risks?
• What other options are available?
• How do the risks and possible benefits of this trial compare with those options?

Participation and care

• What kinds of therapies, procedures and/or tests will I have during the trial?
• Will they hurt, and if so, for how long?
• How do the tests in the study compare with those I would have outside of the trial?
• Will I be able to take my regular medications while taking part in the clinical trial?
• Where will I have my medical care?
• Who will be in charge of my care?

Personal issues

• How could being in this study affect my daily life?
• Can I talk to other people in the study?

Cost issues

• Will I have to pay for any part of the trial such as tests or the study drug?
• If so, what will the charges likely be?
• What is my health insurance likely to cover?
• Who can help answer any questions from my insurance company or health plan?
• Will there be any travel or child care costs that I need to consider while I am in the trial?

Tips for asking your doctor about trials

• Consider taking a family member or friend along for support and for help in asking questions or recording answers.
• Plan what to ask — but don't hesitate to ask any new questions.
• Write down questions in advance to remember them all.
• Write down the answers so that they’re available when needed.
• Ask about bringing a tape recorder to make a taped record of what's said (even if you write down answers).

This information courtesy of Cancer.gov.

How is my safety protected?

Ethical guidelines

The goal of clinical research is to develop knowledge that improves human health or increases understanding of human biology. People who take part in clinical research make it possible for this to occur. The path to finding out if a new drug is safe or effective is to test it on patients in clinical trials. The purpose of ethical guidelines is both to protect patients and healthy volunteers, and to preserve the integrity of the science.

Informed consent

Informed consent is the process of learning the key facts about a clinical trial before deciding whether to participate. The process of providing information to participants continues throughout the study. To help you decide whether to take part, members of the research team explain the study. The research team provides an informed consent document, which includes such details about the study as its purpose, duration, required procedures, and who to contact for various purposes. The informed consent document also explains risks and potential benefits.

If you decide to enroll in the trial, you will need to sign the informed consent document. You are free to withdraw from the study at any time.

Most, but not all, clinical trials in the United States are approved and monitored by an Institutional Review Board (IRB) to ensure that the risks are minimal when compared with potential benefits. An IRB is an independent committee that consists of physicians, statisticians, and members of the community who ensure that clinical trials are ethical and that the rights of participants are protected. You should ask the sponsor or research coordinator whether the research you are considering participating in was reviewed by an IRB.

Further reading

For more information about research protections, see:

• Office of Human Research Protection
• Children's Assent to Clinical Trial Participation

For more information on participants’ privacy and confidentiality, see:

• HIPAA Privacy Rule
• The Food and Drug Administration, FDA’s Drug Review Process: Ensuring Drugs Are Safe and Effective

For more information about research protections, see: About Research Participation

What happens after a clinical trial is completed?

After a clinical trial is completed, the researchers carefully examine information collected during the study before making decisions about the meaning of the findings and about the need for further testing. After a phase I or II trial, the researchers decide whether to move on to the next phase or to stop testing the treatment or procedure because it was unsafe or not effective. When a phase III trial is completed, the researchers examine the information and decide whether the results have medical importance.

Results from clinical trials are often published in peer-reviewed scientific journals. Peer review is a process by which experts review the report before it is published to ensure that the analysis and conclusions are sound. If the results are particularly important, they may be featured in the news, and discussed at scientific meetings and by patient advocacy groups before or after they are published in a scientific journal. Once a new approach has been proven safe and effective in a clinical trial, it may become a new standard of medical practice.

Ask the research team members if the study results have been or will be published. Published study results are also available by searching for the study's official name or Protocol ID number in the National Library of Medicine's PubMed® database .

How does clinical research make a difference to me and my family?

Only through clinical research can we gain insights and answers about the safety and effectiveness of treatments and procedures. Groundbreaking scientific advances in the present and the past were possible only because of participation of volunteers, both healthy and those with an illness, in clinical research. Clinical research requires complex and rigorous testing in collaboration with communities that are affected by the disease. As research opens new doors to finding ways to diagnose, prevent, treat, or cure disease and disability, clinical trial participation is essential to help us find the answers.

This page last reviewed on October 3, 2022

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Patient discussion about research.

Q. Where can I get updates about new researches on fibromyalgia? I have fibromyalgia and I would like to know all there is to know and see if they found new breakthroughs on the subject. A. I use this site: http://www.nfra.net/ the main issue of the site is Fibromyalgia research…

Q. I need to do an interview with someone with knowledge on lupus for a research paper any takers? a couple of questions should do it. it doesn't have to be extensive. A. I HAVE SLE AND A FUW MORE THANS THAT ARE KNOW TO BE KNOW TO COME FROM HAVEING SLE LUPUS I AM NOT 100% OF ALL THAT COMES WITH SLE BUT I AM WILLING TO TELL U ALL I KNOW THANK YOU

Q. i have heard that number of scientists found out in one of there researches that breasts Cancer is capable to just disappear with out a treatment , have any one read this article/research ? or maybe just heard about it ? because it is interesting why and how this result happens ... A. hi pinkofdestiny - try also these links, i know and read a lot about the books of Phillip Day and recommend them to everybody. cancer can be healed and there are also ways to make with success prevention: http://www.credencegroup.co.uk/Eclub/ses/sessearch.php?q=breast+cancer&pvdc=0 before a woman should loose her breast, she should make a therapy with vitamine B17 - the vitamin which can eliminate cancer in any form, but you should not know about it! it is terrible, but it is the way how politicians and industry-trust treat us.

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The Importance of Medical Research

Dr. Peace Chikezie

Published 01 Jun 2022 - Updated 17 Mar 2023

Introduction

Every treatment, intervention, medication, way of care, and aftercare in the medical field or health care system came from discoveries. This high quality of care we can experience today was not discovered overnight, but rather through years of effort by medical professionals who investigated the risk factors, causes, preventions, and treatments of diseases. This type of investigation is known as medical/health research. 

The general definition of research is, 'an investigation that is intentionally designed to help develop or contribute to knowledge'. When you add a medical purpose to 'research', the general definition stays the same, but the goal becomes more specific. Ultimately, the goal shifts to a focus on increasing medical knowledge, improving patient care, developing new medicines or procedures, and enhancing the already existing medicines and procedures.

Forms of Medical Research

There are several forms of medical research being conducted today. Here are 3 common forms:

• Basic or Laboratory-based research: This is usually conducted in a laboratory where chemical interactions of biological materials are observed in a controlled environment. For most researchers, this is the first step toward developing methods or products that can be used in other forms of research studies.
• Clinical Trials: This is perhaps the most familiar form of healthcare research. Often, patients volunteer to participate in these studies to test the efficacy and safety of new medical interventions. Alternatively, medical interventions on participants may not be used, but only observation instead.
• Epidemiological Research: An increasingly large portion of health research is now information based. A great deal of research entails the analysis of data and biological samples that were initially collected for diagnostic, treatment, or billing purposes, or that were collected as part of other research projects, and are now being used for new research purposes. This secondary use of data is a common research approach in fields such as epidemiology, health services research, and public health research, and includes analysis of patterns of occurrences, determinants, and natural history of the disease; evaluation of health care interventions and services; drug safety surveillance; and some genetic and social studies

The Importance of Research in Medicine

Why is research important in medicine? The simple answer is that medical research has led to many medical breakthroughs and developments. It would also strongly contribute to shaping the future of medicine.

Here's how:

A. Medical research importance in disease diagnosis:

Medical research has led to the development of diagnostic tools and technologies that allow for earlier and more accurate diagnoses of diseases. 

For instance, breast cancer is one of the most common cancers worldwide. Medical research led to the development of an effective screening method known as mammography which has resulted in earlier detection and a 20% fall in mortality rates. 

Another example is the development of pap smears for the early diagnosis of cervical cancer. This as well as caused a significant decrease in late presentation and mortality rates due to cervical cancer.

A host of other effective screening methods have been developed as a result of medical research such as genetic testing, imaging techniques, and so on. 

B. The importance of medical research in innovative treatments

Medical research has led to the development of new treatments for a wide range of diseases, such as cancer, allergies, HIV/AIDS, heart disease, and so on.

Research is essential to find out what treatments work best, and more specifically what treatments work best for what patient. It can provide important information about how effective a medical intervention is and its possible adverse effects. These interventions include drugs, vaccines, medical devices, and others. 

By being specific with participant requirements, medical professionals can study how certain groups of people react to certain treatments . An example of this can be seen here at Infiuss Health. As a CRO in Africa, we at Infiuss Health focus on the demographics of the continent to ensure people of African ancestry receive effective care.

Medical research would lead to newer developments in medicine such as personalized medicine and targeted therapies, that would ensure that each individual would have treatment options unique to them. Increasing research in this area is the only way to make this a reality in the future of medicine.

C. The role of medical research in disease prevention

Medical research has contributed to the prevention of diseases such as polio, smallpox, and measles which caused the deaths of millions of people in the past.

Recently, following the Covid-19 pandemic, medical research led to the development of vaccines that gradually slowed down the progress of the disease. 

D. The importance of medical research in public health

Medical research has contributed to our understanding of public health issues and how to address them.

A typical example was in 1854 when there was an outbreak of cholera in the Golden Square Area in London. An Anaesthesiologist known as John Snow conducted an epidemiological study and found that the source of contamination was a public pump. When the contaminated pump was closed from public access, the outbreak of cholera ended. 

Research provides important information about disease trends and risk factors, outcomes of treatment or public health interventions, functional abilities, patterns of care, and health care costs and use. 

E. Medical research's importance in improving the economy:

Economists have found that medical research can have an enormous impact on the quality of healthcare which in turn affects human health and longevity.

Healthy individuals tend to be more productive and that contributes greatly to the national economy. If the research enterprise is impeded, or if it is less robust, important societal interests are affected.

Covid-19 vaccine development, for example, contributed to the lifting of the lockdown in many countries and allowed individuals to resume work.

Compared to treatment, current research on disease prevention shows that preventive services are able to significantly reduce deaths and illnesses at reasonable costs. All of these findings have informed and influenced national budget planning and policy decisions.

The simple fact is that clinical research improves our lives. It leads to significant discoveries, improves health care, and ensures that patients receive the best care possible. It is what makes the development of new medicines and treatments possible, without it we would not be able to move forward in the development of medicine.

Infiuss Health, as a CRO in Africa, aims to make it easier to do more clinical trials/ medical research in Africa by use of technology and other means.

When you support, participate in, or conduct medical research, you are helping to continue to build the future of medicine.

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Institute of Medicine (US) Clinical Research Roundtable; Tunis S, Korn A, Ommaya A, editors. The Role of Purchasers and Payers in the Clinical Research Enterprise: Workshop Summary. Washington (DC): National Academies Press (US); 2002.

The Role of Purchasers and Payers in the Clinical Research Enterprise: Workshop Summary.

• Hardcopy Version at National Academies Press

Appendix V Definitions of Clinical Research and Components of the Enterprise

• DEFINITION OF CLINICAL RESEARCH

(Clinical Research: A National Call to Action, November 1999) Clinical research is a component of medical and health research intended to produce knowledge valuable for understanding human disease, preventing and treating illness, and promoting health. Clinical Research embraces a continuum of studies involving interactions with patients, diagnostic clinical materials or data, or populations in any of the following categories: (1) disease mechanisms (etiopathogenesis); (2) bi-directional integrative (translational) research; (3) clinical knowledge, detection, diagnosis and natural history of disease; (4) therapeutic interventions including development and clinical trials of drugs, biologics, devices, and instruments; (5) prevention (primary and secondary) and health promotion; (6) behavioral research; (7) health services research, including outcomes, and cost-effectiveness; (8) epidemiology; and (9) community-based and managed care-based trials.

• MAJOR COMPONENTS OF THE CLINICAL RESEARCH ENTERPRISE

Sponsors include private and public sector funding organizations such as the National Institutes of Health, pharmaceutical companies, medical device manufacturers, biotechnology firms, universities, private foundations, and national societies. Within the public sector the National Institutes of Health (NIH) is the largest clinical research sponsor, followed by the Department of Defense (DOD), the Department of Veterans Affairs (VA), Agency for Healthcare Research and Quality (AHRQ), and the Centers for Disease Control (CDC).

Research Organizations

Research organizations include academic health centers, private research institutes, survey research organizations, federal government intramural research programs, and contract research organizations.

Investigators

Investigators are the scientists performing clinical research from varied disciplines with a range of academic qualifications (e.g., MD, Ph.D., RN, DDS, PharmD).

Participants

Participants are the human volunteers, medical information and biological materials of human origin, or data derived from volunteers. Participants may have particular health conditions or may be healthy volunteers or populations at large.

Oversight Entities

Oversight entities include Institutional Review Boards, Food and Drug Administration, Department of Health and Human Services, Veterans Affairs, National Committee for Quality Assurance, and other national regulatory agencies.

Stakeholders/Consumers

Stakeholders/Consumers include health insurers, managed care organizations, health care systems, organized medicine, voluntary health agencies, patient advocacy groups, purchasers of health care, and providers of health care, public health systems, and individual consumers.

• Cite this Page Institute of Medicine (US) Clinical Research Roundtable; Tunis S, Korn A, Ommaya A, editors. The Role of Purchasers and Payers in the Clinical Research Enterprise: Workshop Summary. Washington (DC): National Academies Press (US); 2002. Appendix V, Definitions of Clinical Research and Components of the Enterprise.
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Clinical Research Acronyms and Abbreviations You Should Know

• Meghan Hosely Marketing Content Manager;

We all know there are numerous acronyms and abbreviations used in clinical research. While some can be easily deciphered, others may take some searching to find their meaning. Particularly with the recent surge in electronic systems and regulations, it can be hard to keep track of necessary abbreviations and terms.

Whether you’re new to the clinical research world or need a refresher, here’s a condensed list of common acronyms and abbreviations you may come across.

AACI: Association of American Cancer Institutes

AAHRPP: Association for the Accreditation of Human Research Protection Programs

ABSA: Association of Biosafety and Biosecurity

ACRP: Association of Clinical Research Professionals

ACTS: Association for Clinical and Translational Science

ADME: Absorption, Distribution, Metabolism, and Elimination

ADR: Adverse Drug Reaction

AE: Adverse Event

ALCOA:  Attributable, Legible, Contemporaneous, Original, Accurate

AMC: Academic Medical Center

API: Active Pharmaceutical Ingredient

API: Application Program Interface

ARO: Academic Research Organization

ASCO: American Society of Clinical Oncology

ASCPT: American Society for Clinical Pharmacology and Therapeutics

ASGCT: American Society of Gene & Cell Therapy

BA/BE: Bioavailability/Bioequivalance

BLA: Biological Licensing Application

BSM: Biospecimen Management

caBIG: Cancer Biomedical

CAPA:  Corrective and Preventive Action

CAR-T: Chimeric Antigen Receptors and T cells

CBER: Center for Biologics Evaluation and Research

CBRN: California Board of Registered Nursing

CCEA: Complete, Consistent, Enduring, Available

CCOP: Community Clinical Oncology Program

CCR: Center for Cancer Research

CCSG: Cancer Center Support Grant

CCTO or CTO: Centralized Clinical Trials Office or Clinical Trials Office

CDASH:  Clinical Data Acquisition Standards Harmonization

CDER: Center for Drug Evaluation and Research

CDM: Clinical Data Management

Related article: “Improve Data Quality with 5 Fundamentals of Clinical Data Management”

CDP: Clinical Development Plan

CDRH: Center for Devices and Radiological Health

CDS: Clinical Data System

CDUS: Clinical Data Update System

CFR: Code of Federal Regulations

CMO: Contract Manufacturing Organization

CMS: Centers for Medicare & Medicaid Services

CRA: Clinical Research Associate

CRC: Clinical Research Coordinator

Related article: “Deciphering the CRC Career Path: Key Skills and Responsibilities”

CRF: Case Report Form

CRMS: Clinical Research Management System

CRO: Contract Research Organization

CRPC: Clinical Research Process Content

CSO:  Contract Safety Organization

CSR: Clinical Study Report

CTA: Clinical Trial Authorization

CTCAE: Common Terminology Criteria for Adverse Events

CTMS: Clinical Trial Management System

CTRP: Clinical Trials Reporting Program

CTSA: Clinical and Translational Science Award

DDI: Drug-Drug Interaction

DHHS: Department of Health and Human Services

DM: Data Manager

DMC: Data Monitoring Committee

DSMB: Data and Safety Monitoring Board

EC: Ethics Committee

eCOA: Electronic Clinical Outcome Assessment

eCRF: Electronic Case Report Form

EDC: Electronic Data Capture

Learn more about Advarra’s electronic data capture system, Advarra EDC .

EHR: Electronic Health Record

EMR: Electronic Medical Record

ePRO: Electronic Patient-Reported Outcomes

eTMF: Electronic Trial Master File

FAIR: Findable, Accessible, Interoperable, Reusable

FDA: Food and Drug Administration

FE: Food Effect

FIH: First In Human

FWA: Federalwide Assurance

GCP: Good Clinical Practice

GCRC: General Clinical Research Center

GDP:  Good Documentation Practice

GLP: Good Laboratory Practice

GMP: Good Manufacturing Practice

GVP: Good Pharmacovigilance Practice

HIPAA: Health Insurance Portability and Accountability Act

HRPP: Human Research Protection Program

IBC: Institutional Biosafety Committee

ICF: Informed Consent Form

ICH: International Council for Harmonization

IDE: Investigational Device Exemptions

IEC: Independent Ethics Committee

IHCRA: In House Clinical Research Associate

IIT: Investigator Initiated Trial

IND: Investigational New Drug (Application)

IP: Investigational Product

IRB: Institutional Review Board

ITT: Intent to Treat

IVRS: Interactive Voice Response System

IWRS: Interactive Web Response System

LTFU: Long Term Follow Up

LRAA: Local Regulatory Affairs Associate

MAC: Medicare Administrative Contractor

MAD: Multiple Ascending Dose

MCA: Medicare Coverage Analysis

Related webinar: Build a Better Budget: Using Medicare Coverage Analysis to Streamline Study Startup .

MRN: Medical Record Number

NCI: National Cancer Institute

NDA: New Drug Application

NHV: Normal Healthy Volunteer

NIH: National Institutes of Health

NLM: National Library of Medicine

OCT: Office of Clinical Trials

OHRP: Office for Human Research Protections

OSR: Outside Safety Report

PAC: Post Approval Commitments

PC: Protocol Coordinator

PD: Protocol Director

PHI:  Protected Health Information

PI: Principal Investigator

PK/PD: Pharmacokinetic/Pharmacodynamic

PRE: Prompt Reporting Event

PRMC: Protocol Review and Monitoring Committee

PRMS: Protocol Review and Monitoring System

QC: Quality Control

QCT: Qualifying Clinical Trial

QMS: Quality Management System

SAD: Single Ascending Dose

SAE: Serious Adverse Event

SC: Study Coordinator

SDR: Source Document Review (Also Source Data Review)

SDTM:  Study Data Tabulation Model

SDV: Source Document Verification

SIF: Site Investigator File

SMO: Site Management Organization

SOC: Standard of Care

SOE: Schedule of Events

SOP: Standard Operating Procedure

Related article: “Data Collection in Clinical Trials: 4 Steps for Creating an SOP”

SPOREs: Specialized Programs for Research Excellence

SRB: Scientific Review Board

SRC: Scientific Review Committee

SUSAR: Suspected Unexpected Serious Adverse Reaction

SVT: Subject Visit Template

TMF: Trial Master File (also eTMF)

TMO: Trial Management Organization

UADE: Unanticipated Adverse Device Effect

UADR: Unexpected Adverse Drug Reaction

UAP: Unanticipated Problem

Is there an abbreviation or acronym you see regularly in clinical research that isn’t listed here? Let us know .

To learn more about the fundamentals of clinical research, check out these free resources:

• Improve Data Quality with 5 Fundamentals of Clinical Data Management
• Beginner’s Guide to Clinical Trial Performance Metrics
• Deciphering the CRC Career Path: Key Skills and Responsibilities

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Meghan Hosely creates educational content for Advarra, such as blogs, eBooks, white papers, and more.

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A teacher-guided early-learning method for medical image segmentation from noisy labels

• Original Article
• Open access
• Published: 13 August 2024

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• Shangkun Liu 1 ,
• Minghao Zou 1 ,
• Ning Liu 1 ,
• Yanxin Li 1 &
• Weimin Zheng   ORCID: orcid.org/0000-0003-3860-4208 1 , 2  

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The success of current deep learning models depends on a large number of precise labels. However, in the field of medical image segmentation, acquiring precise labels is labor-intensive and time-consuming. Hence, the challenge of achieving a high-performance model via datasets containing noisy labels has attracted significant research interest. Some existing methods are unable to exclude samples containing noisy labels and some methods still have high requirements on datasets. To solve this problem, we propose a noisy label learning method for medical image segmentation using a mixture of high and low quality labels based on the architecture of mean teacher. Firstly, considering the teacher model’s capacity to aggregate all previously learned information following each training step, we propose to leverage a teacher model to correct noisy label adaptively during the training phase. Secondly, to enhance the model’s robustness, we propose to infuse feature perturbations into the student model. This strategy aims to bolster the model’s ability to handle variations in input data and improve its resilience to noisy labels. Finally, we simulate noisy labels by destroying labels in two medical image datasets: the Automated Cardiac Diagnosis Challenge (ACDC) dataset and the 3D Left Atrium (LA) dataset. Experiments show that the proposed method demonstrates considerable effectiveness. With a noisy ratio of 0.8, compared with other methods, the mean Dice score of our proposed method is improved by 2.58% and 0.31% on ACDC and LA datasets, respectively.

Explore related subjects

• Artificial Intelligence
• Medical Imaging

Avoid common mistakes on your manuscript.

Introduction

Background. Currently, the technology based on machine learning and deep learning is constantly advancing and evolving, and has been successfully applied to many fields [ 1 , 2 ], including medical image analysis [ 3 ]. These applications include the assessment of diseased and normal images, as well as the detection and segmentation of diseased organs. However, the effectiveness of convolutional neural networks (CNNs) based on supervised learning relies heavily on precise annotation. Unlike natural images, the features of different types of medical images have different meanings. That is, it is necessary to collect their corresponding accurate pixel-level labels for medical image segmentation [ 4 ]. However, due to the poor interpretability, pixel-level labeling of medical images often necessitates expert annotation and consumes a substantial amount of time. In contrast, rough labeling of the target not only does not require a large number of professionals, but also takes significantly less time, thereby reducing labeling costs.

Challenges. Based on the above, we naturally expect the segmentation model could achieve excellent performance based on rough annotations. However, the learning process of traditional supervised learning methods completely trusts the given labels [ 5 ]. The model learns noisy labels and clean labels indiscriminately, which makes it difficult to learn accurate information. Previously, noisy label learning has been well studied in classification tasks [ 6 , 7 ]. However, in the segmentation task, each pixel has its corresponding label. The huge number of labels makes it more difficult to the study of noisy label learning.

Especially for medical image segmentation, the accuracy of target edge segmentation is higher. And target edges are also the most likely to be mislabeled. Therefore, it is worth studying how to obtain good segmentation performance in the case of rough labeling.

For the noisy label learning in segmentation task, researchers have done some research on the loss function and the structure of the model. Zhang et al. [ 8 ] proposed a noise-aware loss (NAL), which reduces the impact of noisy labels on the network performance. In addition to studying the robust loss function, Wang et al. [ 9 ] proposed a method called meta corrupted pixels mining based on a simple network. And to improve the tolerance of the model to noisy labels, some studies employ multiple networks for alternate learning and selectively choose clean samples for training [ 10 , 11 ]. Fang et al. [ 12 ] not only leverage the cooperation between two models but also incorporate an enhanced consistency constraint. This constraint facilitates the extraction of specific reliable knowledge from each model and integrates it into the other model. Such methods mitigate the impact of noisy labeled data by selecting clean data to learn. While these methods pay significant attention to clean data, there remains underutilization of data containing noisy labels.

Interestingly, the researchers found that during the learning process, CNNs tend to learn clean samples in the early stage, and fit wrong samples in the later stage. So in order to use all the samples, Liu et al. [ 13 ] made use of this learning feature and proposed the model named ADELE to correct pixel-level noisy labels while training. However, the learning mode that relies on a single network to correct noisy labels while training is easy to cause the accumulation of errors. And the study found that for the same dataset containing noisy labels, it can achieve better results by treating low quality (LQ) and high quality (HQ) labels separately [ 14 ]. Inspired by semi-supervised learning, some work applies the branch architecture to noisy label learning. Samples with LQ and HQ labels enter different branches, and the branches guide each other to learn [ 15 ]. The methods of differentiating between HQ and LQ labels have yielded promising results. These methods not only maximize the utilization of information within HQ labels but also effectively captures pertinent information from LQ labels. However, in practical scenarios, acquiring a dataset capable of distinguishing between HQ and LQ labels proves challenging. Consequently, these methods put additional constraints on the datasets.

Our contributions. In order to solve the above problems, we propose a noisy label learning method based on mixed learning of HQ and LQ samples. According to reference [ 13 ], we know that models tend to fit clean labels first during the learning process. Using this property, the model can correct the noisy label while training. However, relying on a single network is too easy to accumulate errors.

Inspired by mean teacher [ 16 ], the weight of the teacher model is obtained from the exponential moving average (EMA) of the student model. Based on this, we believe that the teacher model is closer to the “early-learning" stage than the student model. Therefore, to avoid error accumulation, we add a teacher model to guide the correction of noisy labels during training.

In computer vision, image-level perturbation of input images is the most common way of data enhancement [ 18 ]. The disturbed image is similar to the original image but not exactly the same. Common methods of image perturbation include Gaussian blur, reduction, rotation, inversion and grayscale. However, these methods are completely limited to the image level and only increase the diversity of the training set through small geometric transformations or color transformations. With the development of semi-supervised learning, some work has also explored the positive effects of feature level perturbations on models [ 19 ]. Compared with image perturbation, feature perturbation provides a wider disturbance space and can enhance the stability of the model. Noisy label learning is similar to semi-supervised learning in that they both carry imperfect labels. So we introduce feature perturbations into our work.

Therefore, our proposed method is an architecture based on early-learning and MT for mixing high-low quality samples on noisy label learning tasks. Our main contributions are as follows.

We propose a new noisy label learning method based on early-learning and mean teacher. We do not distinguish between HQ and LQ samples, which makes it more compatible with various datasets with noisy labels. According to the early learning characteristics of CNNs, we propose to use teacher model to correct the noisy labels in datasets when conditions are met.

Inspired by the unified dual-stream perturbations method in the semi-supervised field, the feature perturbation is added to the student model to enhance the robustness of the framework, so that it is not easy to fit the noisy labels.

We conducted experiments on the Automated Cardiac Diagnosis Challenge (ACDC) dataset and the 3D Left Atrium (LA) dataset. The results show that our method outperforms the methods that do not distinguish between high and low quality samples.

Related work

• Medical image segmentation

Medical image segmentation plays a crucial role in the analysis of medical imagery and serves as a vital technology for assisting diagnostic procedures. It accurately extracts regions of interest from the background, such as organs, tumors, and blood vessels.

The success of CNNs has also driven the development of medical image segmentation. U-Net [ 20 ], one of the most classical model, was proposed in 2015. Characterized by its distinctive U-shaped architecture, it can extract both deep and superficial features from images with remarkable effectiveness. As a result, U-Net [ 20 ] and its variants [ 21 , 22 , 23 ] have undergone extensive research and have been widely implemented in the domain of medical image segmentation. Building on the Transformer’s triumph in natural language processing, some studies [ 24 , 25 , 26 ] have integrated it with U-Net to enhance the precision of target area segmentation. Yuan et al. [ 27 ] fully exploited the characteristics of Transformer and CNNs, thereby proposed a network combining the complementary features of the two frameworks. By fusing features from the distinct domains of CNNs and Transformers, the method enhances feature representation capabilities and demonstrates robust performance across various medical image segmentation datasets. Nevertheless, the impressive performance of models based on supervised learning is contingent upon the availability of precise pixel-level annotations.

Given that medical image labels are harder to acquire compared to those of natural images, there is an increased likelihood of encountering incomplete or incorrect annotations. To address the issue of missing labels, researchers have developed solutions that involve designing semi-supervised learning architectures [ 14 , 28 , 29 ]. Because the target of medical image is not clear, the pixels in the edge area of the segmentation target are more likely to be classified incorrectly. Due to the often ambiguous nature of targets in medical imaging, pixels located in the border regions of the segmentation target are more susceptible to misclassification. To solve this problem, noisy label learning for medical image segmentation has attracted much attention from researchers [ 12 , 30 , 31 ]. In our study, we propose an effective noisy label learning method for medical image segmentation.

• Noisy label learning

With the rapid development of deep neural networks, models trained with precise label supervision have attained remarkable performance. Generating a large number of precise labels is a time-consuming and labor-intensive process, particularly in specialized domains like medical imaging, where expert professionals are required to create these annotations. Therefore, noisy label learning has gradually been paid attention by researchers [ 32 , 33 ]. At present, the related work on noisy label learning for classification tasks is relatively comprehensive.

Initially, researchers aimed to accommodate noisy labels by modifying the architecture and loss functions of CNNs. Goldberger et al. [ 34 ] proposed to add an extra softmax layer to the end of their model to characterize the noisy labels. Likewise, a noise layer, whose parameters are initially unknown, is integrated into the design, allowing these parameters to be learned concurrently with the neural network’s parameters during training [ 35 ]. In order to accommodate to noisy label learning, a generalized cross entropy (GCE) loss function [ 36 ] was proposed. Ma et al. [ 37 ] also conducted an examination into the robustness of loss functions that are widely employed. Furthermore, the active passive loss was proposed, which consists of two robust loss functions that can promote each other. While these methods can suppress simple noisy labels, such as symmetric ones, their capacity to withstand intricate and irregular noisy labels remains comparatively limited.

To minimize the impact of samples with noisy labels on the training process, researchers aim to refine sample selection methods that encourage the model to predominantly learn from clean labels. The Co-teaching [ 10 ] involves the collaborative training of two neural networks simultaneously. Each of the two networks selects the samples with lower loss with respect to the other network in order to effectively address the issue of noisy labels. Song et al. [ 38 ] proposed a two-step training process for their model to prevent overfitting on data that contains noisy labels. In the initial phase, all samples are utilized to update the network and a subset of clean samples are identified. Subsequently, in the later phase, the model exclusively employs the samples deemed clean for continued training. These methods effectively leverage clean labeled data to enhance model performance. However, they overlook the valuable information embedded within noisy labeled data.

To acquire a larger, cleaner dataset for training in scenarios where datasets have noisy labels, the LongReMix [ 39 ] was introduced. This method first obtains a clean sample set, and then samples and oversamples it to increase the size of the clean sample set. In order to make full use of samples, Zheng et al. [ 40 ] proposed a method of meta-label correction. This approach treats the rectification of noisy labels as a meta-learning task, wherein a meta-model is employed to generate refined labels for the primary model. Research has indicated that the application of contrastive learning and hybrid attention mechanisms [ 41 ] can effectively mitigate the impact of label noise and subsequently enhance the overall performance of the model.

Noisy label learning for segmentation

Image segmentation is indeed a more complex task compared to image classification, it requires labels for all pixels in an image. The large number of labels also makes it more prone to producing incorrect annotations. Noisy label learning in image segmentation is a critical area of research because noisy, inaccurate, or mislabeled data can significantly degrade the performance of segmentation models.

Intuitively, similar to classification tasks, researchers aim for models to discern clean labels within noisy datasets. Hence, references [ 10 , 38 ] and etc. are also applicable to image segmentation. Taking inspiration from the Co-teaching [ 10 ], Zhang et al. [ 11 ] introduced an innovative “Tri-network" learning framework to better deal with pixel-level noisy labels. This framework encompasses three networks that cyclically assume the role of a teacher, guiding the learning process of the other networks. Inspired by Co-learning [ 42 ], Fang et al. [ 12 ] proposed an algorithm that enables two models to engage in complementary co-training, thereby improving their learning efficiency and accuracy. In this method, a sophisticated distillation module is designed to purify superior-quality knowledge and enhance the use of reliable knowledge. This kind of sample selection method mitigates the influence of inaccurate labels on the model. Nevertheless, Liu et al. [ 43 ] explored the influence of noisy labels at different layers in supervised model training and affirmed that noisy labels contain valuable information. Therefore, the sample selection methods concurrently necessitate the sacrifice of valuable insights that might be embedded within noisy labels.

Given the significant correlation between pixel-level labels in image segmentation, researchers extensively exploit this information. To leverage the inter-pixel information for the purpose of mitigating the effect of noisy labels, Guo et al. proposed a joint class-affinity segmentation (JCAS) framework [ 44 ]. This method rigorously exploits the inherent affinity relationships that exist intra-class and inter-class to enhance the accuracy and robustness of the model. According to Li et al., it is posited that in the context of image segmentation, noisy labels are predominantly encountered proximal to the boundaries of objects [ 45 ]. Therefore, a robust learning strategy guided by superpixels is proposed. Guo et al. [ 46 ] propose a noise-label tolerant SAC-Net and use this network for two-stage training. While designing a structure resilient to noisy labels can enhance their tolerance, their adverse impact on the training process persists.

For noisy label learning in medical image segmentation, researchers have also done related studies. For the fetal brain tissue segmentation task, Karimi et al. [ 47 ] developed a new label smoothing procedure and loss function to train a model. This method properly considers the uncertainty of the target boundary. To improve the robustness of noisy label learning models for retinal blood vessel segmentation, Zhou et al. [ 48 ] developed the study group learning (SGL) method. In datasets annotated by various non-expert individuals, a method using two coupled networks was proposed. The first network is devised to estimate the true segmentation probability and the other to model the annotation propensity by estimating a pixel-by-pixel confusion matrix. Similarly, Liao et al. [ 49 ] proposed preference-involved annotation distribution learning distribution learning (PADL) framework. In addition to the above methods, Liu et al. [ 13 ] used the learning characteristics of deep learning to propose the method to gradually correct pixel-level noisy labels during the training process. To improve the accuracy of correcting noisy labels, Liu et al. [ 50 ] proposed contour information as an auxiliary tool to correct inaccuracies in pixel-level labels. Depending solely on a single model to correct noisy labels during training can lead to error accumulation. We introduce a teacher model to provide guidance in correcting noisy labels, thereby reducing the likelihood of error accumulation.

Inspired by previous works, researchers have proposed work on noisy label learning based on teacher-student structure. Zhang et al. [ 51 ] developed a framework for medical image segmentation that effectively mitigates the detrimental effects of noisy labels by engaging a novel teacher-student network architecture. The teacher network is initially trained using the original dataset, notwithstanding the presence of noisy labels. Subsequently, it leverages confidence learning coupled with spatial label smoothing regularization to refine the noisy annotations. The student network is then trained on these purified labels. This approach systematically diminishes the influence of noisy labels on the model’s performance. Nevertheless, the model needs to go through two stages of training, which increases the training time. In addition, in references [ 14 , 15 , 52 ], strong annotations and weak annotations within the dataset are discretely processed, ensuring that the information contained within the strongly annotated segment is exhaustively extracted and employed. However, these methods increase the requirements of the datasets. In practice, acquiring a dataset capable of effectively discerning between HQ and LQ labels poses a significant challenge. So, our proposed method adopts a teacher-student architecture for mixing HQ and LQ samples to approximate real-world datasets with noisy labels.

Framework overview

The framework of our proposed method. The mixture of HQ and LQ samples is simultaneously fed into the student model and teacher model. The student model generates two outputs through feature perturbation. FP denotes the feature perturbation. The prediction of teacher model and the original label are processed by confidence learning and label refinement to yield the denoised label. When certain conditions are met, part of the pixel-level annotations in the original label are corrected

To adapt to a wider range of datasets with noisy labels, we propose a noisy label learning method with mixed learning of HQ and LQ samples, as shown in Fig.  1 .

Unlike some methods [ 14 , 15 ] for noisy label learning, our proposed method is designed to operate even when the HQ samples are unknown. Here, we assume that the entire training set consists of M samples, of which N samples belong to the samples labeled with HQ, and the remaining \((M-N)\) samples is labeled with LQ. For the sake of clarity and convenience, we will denote the samples labeled with high quality as \(\phi _h = {(\textbf{X}_{(i)}, \textbf{Y}_{h(i)})}^N_{i=1}\) , where \(\textbf{X}_i\) represents an image and \(\textbf{Y}_{h(i)}\) represents the HQ label corresponding to \(\textbf{X}_i\) . Similarly, samples labeled with LQ are represented as \(\phi _l = {(\textbf{X}_{(i)}, \textbf{Y}_{l(i)})}^{M}_{i=N+1}\) , where \(\textbf{Y}_{l(i)}\) is the label with LQ. In addition, \(\textbf{X}_{(i)} \in \mathbb {R}^{\Omega _i}\) , and \(\textbf{Y}_{h(i)}, \textbf{Y}_{l(i)} \in \{0,1\}^{\Omega _i}\) .

In a realistic scenario, it becomes challenging to distinguish between samples with HQ and LQ labeling if no specific treatment is applied. Therefore, We mix \(\phi _h\) and \(\phi _l\) as \(\phi _{mix} = {(\textbf{X}_{(i)}, \textbf{Y}_{mix(i)})}^M_{i=1}\) in random order during training. In this way, the samples of HQ and LQ are not distinguished, and are respectively sent into the teacher and student models. As with the MT, a small perturbation noise \(\zeta \) is added to the image when it is fed into the teacher model.

Previous research [ 13 , 53 ] has demonstrated that leveraging the learning characteristics of deep learning can aid in correcting noisy labels within the dataset during training, thereby maximizing the utilization of all the samples. However, relying solely on a single network to simultaneously train and correct may lead to error accumulation. Inspired by multi-model learning methods such as Co-teaching [ 10 ], we hope to make use of the early-stage learning characteristics of the model and make another model guide the noisy label learning process. In the field of semi-supervised learning, mean teacher [ 17 ] has proven to be an effective architecture. It consists of two models: a student model and a teacher model.

The weight of the teacher model is determined through the EMA of the student model, and the specific update strategy is introduced in Sect.  Teacher-guided correction with early learning . The teacher model aggregates all the previously learned information as soon as each step is completed. In this way, the output quality of all layers is improved, and the model can better represent the middle and even high-level semantic information. Based on the update strategy of the teacher model, we can infer that the update of teacher model will be slower than that of student model.

From the view of early learning, the model first memorizes clean labels and then memorizes noisy labels [ 13 , 53 ]. Therefore, after a period of training, the teacher model remembers more clean labels than the student model for the same epoch. Therefore, we use the teacher model to generated the prediction and correct noisy labels in the dataset. To be specific, the prediction of teacher model and original labels are input into the confident learning module, and the original noisy labels are corrected by label refinement.

To enhance the learning capability of the student model, feature perturbation is incorporated. This involves intentionally adding noise or variations to the input features, which encourages the student model to generate two sets of predictions based on the perturbed features. The consistency loss is then computed between these two sets of predictions.

Teacher-guided correction with early learning

Liu et al. [ 13 ] propose to use the inherent characteristics of neural networks, namely “early-learning", to address the issue of noisy label correction during the learning process. However, this method employs a single network for training and correcting the noisy labels. Therefore, inspired on the early-learning, we propose a novel medical image segmentation method that utilizes teacher-guided correction for learning with mixed HQ and LQ data.

(1) Early-learning based on teacher model

The weight of teacher model is obtained by the EMA of the student model. Assuming that the current training epoch is t , the weight of the student model is expressed as \(\theta _t\) , and the weight of the previous epoch is expressed as \(\theta _{t-1}\) . Then the weight of teacher model at the t epoch \(\theta '_{t}\) is calculated as shown in Eq.  1 :

where \(\beta \) is the decay rate and set to 0.99 according to [ 17 ]. \(\theta _{t-1}^{\prime }\) is the weight of teacher model at \(t-1\) epoch.

As illustrated in Fig.  1 , we aim to leverage the teacher model as the “third party" [ 15 ] to guide the identification the noisy labels and generate the refined labels. Therefore, the original labels and the refined labels jointly guide the learning of the student model.

Previous works [ 13 , 53 ] have indicated that models prioritize fitting clean labels during the learning process with noisy labels. From Eq.  1 , it can be seen that the weights of the teacher model aggregate the previously learned information. Therefore, the teacher model is less likely to overfit to noisy labels compared to the student model. Based on this, we propose that teacher model to guide the correction of noisy labels. It is worth noting that the output of the teacher model only serves as a guide to correct noisy labels and does not participate in the updating process of the student model.

In order to guide the correction of noisy labels more effectively, we use confident learning (CL) [ 54 ] to identify error labels at the pixel level and correct the original labels. In this way, we accomplish the objective of reducing noisy labels during the training process.

(2) Identification of pixel-level noisy labels

To identify errors in labels, Northcutt et al. [ 54 ] proposed a belief learning method. Through the estimation of the joint distribution matrix between the noisy labels and the ground truth, the noise in the labels is robustly represented.

Based on the prediction \({P}_t\) of the teacher model and the original noisy label \(\textbf{Y}\) , the error map that can represent the error in the original label is obtained through CL. The following steps are required to obtain the error map \(Map_{err}\) corresponding to image \(\textbf{X}\) .

First, assume that in mask \(\textbf{Y}\) , the pixel x is labeled \(y = u\) . In the prediction result \({P}_t\) of the teacher model, the probability \(\hat{p}_u(x)\) denotes the self-confidence that pixel x belongs to class u . However, if the probability of pixel x being classified into class v is higher than the threshold \(t_v\) in \(P_t\) , it suggests that the pixel is more likely to belong to the class v rather than u . This latent ground truth is represented as \(y^*\) . The calculation of threshold for class v , i.e. \(t_v\) , is shown in the Eq.  2 :

where \(\textbf{X}_{y=v}\) represents the pixels classified as v in the image \(\textbf{X}\) .

According to y and \(y^*\) , the counting matrix \(\textbf{C}\) of confidence is obtained as shown in Eq.  3 :

where \(\mathcal {C}_m \in \{1, 2,..., m\}\) is a collection of label classes, and m is the number of classes.

Secondly, the matrix \(\textbf{C}\) is calibrated to obtain \(\textbf{C}'\) , as shown in the Eq.  4 :

Then, we obtain the joint distribution matrix \(\hat{\textbf{Q}}\) that estimates y and \(y^*\) , as shown in Eq.  5 :

According to the obtained joint distribution matrix \(\hat{\textbf{Q}}\) and the strategy provided in [ 54 ], we employ the “Prune-by-class" approach to identify noisy labels in \(\textbf{Y}\) . Finally, we get the estimate error map \(Map_{err}\) corresponding to the image \(\textbf{X}\) . In this error map, “1" means that the corresponding pixel is incorrectly labeled, and “0" is the opposite.

(3) Adaptive label correction

Based on the estimated error map \(Map_{err}\) obtained through CL, we employ a computationally simple strategy known as hard refinement to process the mask \(\textbf{Y}\) that contains noisy labels. The mask refined by the hard label is represented as \(\textbf{Y}'\) , which is defined as the Eq.  6 :

Due to the nature of deep learning models, it is easier to learn clean labels during the early stages of training. Therefore, we adaptively correct the noisy labels during training process. The refined label \(\textbf{Y}'\) not only serves as a guide for training the student model, but also acts as a reference for correcting the original label \(\textbf{Y}\) under certain conditions. It is necessary to first evaluate whether to start the correction during training. According to [ 13 ], the \(IoU_{train}\) of the outputs \(P_t\) and the original labels \(\textbf{Y}\) increases rapidly during early stage of training. Therefore, the least square method is employed to fit the \(IoU_{train}\) change during training, as shown in the Eq.  7 :

where t represents the iterations of training. \(0< a \le 1\) , \(b \ge 0\) and \(c \ge 0\) are the parameters that need to be fitted. Thus, the \(IoU_{train}\) change curve is obtained. To determine the appropriate time to initiate the correction of the noisy label, we can utilize the derivative of the function f ( t ), denoted as \(f'(t)\) . When \(\frac{\mid f^{\prime }(1)-f^{\prime }(t)\mid }{\mid f^{\prime }(1)\mid }>g\) is satisfied, we begin to correct \(\textbf{Y}\) according to \(\textbf{Y}'\) , where g takes 0.8.

It is essential to note that when dealing with multiple classes in the label, the time to correct each class is calculated individually. The following is the pseudo-code for the aforementioned correction procedure, as presented in Algorithm  1 .

Teacher-guided noisy label correction with early-learning

Student model with feature perturbation

Student model. The the input image is denoted by X , and its features are extracted via the Encoder. This feature is directly fed into the Decoder to yield one prediction. Additionally, the feature enters the Decoder through the feature perturbation to obtain another prediction. FP in the figure represents feature perturbation

For ACDC dataset, the distribution of the IoU between the ground truth and the noisy label for different noisy ratios

We focus more on the robustness of the student model as the teacher model only participates in the correction process of the noise labels. To enhance the model’s learning ability, a method of expanding the perturbation space using auxiliary feature perturbation flow is proposed [ 19 ]. This method does not require additional effort to explore the optimal combinations of perturbations and hyperparameters, and has emerged as a prominent method within the field of semi-supervised learning, offering notable advantages over other methods. Inspired by this, we propose a student model with feature perturbation, as shown in Fig.  2 .

Specifically, the original student model consists of one feedforward stream that yields \(P_s\) . Now, we introduce an additional feedforward stream incorporating a feature perturbation between the Encoder e and the Decoder d in the student model. Through this feedforward stream, we obtain \(P_s^{fp}\) which is defined as follows:

where t refers to the intermediate features extracted by the Encoder from the image \(\textbf{X}\) . F is the feature perturbation. According to the reference [ 19 ], feature perturbation can be effectively achieved by utilizing a simple channel dropout. That means we introduce feature perturbations in the student model without incurring excessive computational overhead. Incorporating the consistency loss between \(P_s\) and \(P_s^{fp}\) to enhance the learning capacity of the student model.

Loss function

The loss function for the method is comprised of three distinct components, denoted as \(\mathcal {L}_c\) , \(\mathcal {L}_1\) and \(\mathcal {L}_2\) , respectively. \(\mathcal {L}_c\) denotes the consistency loss:

where N denotes the number of samples. And \(P_s\) and \(P_s^{fp}\) is two prediction of student model.

\(\mathcal {L}_1\) represents the loss calculation between the output \(P_s\) of the student model and the denoised label as shown in Fig.  1 . And \(\mathcal {L}_2\) represents the loss calculation between \(P_s\) and the label \(\textbf{Y}\) or the corrected label \(\dot{\textbf{Y}}\) . \(\mathcal {L}_1\) and \(\mathcal {L}_2\) consist of cross entropy (CE) loss, dice loss, focal loss and boundary loss. The definition is as follows:

where \(\lambda \) is set to 0.5. The items in Eq.  11 represent the loss between the predicted results of the student model and the labels in the current dataset. And the items in Eq.  12 represent the loss between the predicted results and the denoised labels. According to Eqs.  10 , 11 and 12 , the total loss \(\mathcal {L}\) is expressed as

where \(\mu \) is also set to 0.5. \(\gamma \) is set to a weight that gradually increases with the training period. Specifically, \(\gamma \) uses a Gaussian ramp-up curve [ 55 ]:

where \(\varphi _{max}\) is typically set to 0.8, and \(t_{max}\) represents the maximum number of training iterations.

Datasets and experimental setting

To validate the efficacy of our proposed method, we executed experiments on two distinct medical datasets. To assess the robustness of our method against datasets with imprecise annotations, we introduced noise to the labels within both datasets.

Automated Cardiac Diagnosis Challenge (ACDC) dataset. ACDC leverages dynamic magnetic resonance imaging (MRI) to delineate the anatomical structures of the heart, focusing on the left ventricle (LV), right ventricle (RV), and myocardium (Myo). This dataset encompasses a total of 150 patient cases. Following [ 12 ], we apply the same data processing to the dataset. Specifically, patches with dimensions of 128 \(\times \) 128 pixels are extracted, centering on the heart region. Within this dataset, a subset of 1312 patches is allocated for training, while 380 patches are dedicated to the validation set.

Noisy label generation of ACDC. Since there are three classes in the ACDC dataset, the incidence of labeling inaccuracies is substantially higher. Such discrepancies not only manifest as annotation errors contrasting the targeted segmentation zones against the background but also amongst different target classes themselves. To simulate noisy labels in the segmentation task, we randomly employ a series of image manipulation operations: resizing, deformation, rotation, translation, and morphological dilation of the mask. In the training set of ACDC, we randomly select the samples with proportion r as the samples with noisy labels. In Fig.  3 , we show the distribution of the intersection-over-union (IoU) ratio between the ground truth and the noisy label for different noisy ratios. Figure  4 shows the examples of the original images, the original labels and the noisy labels.

ACDC dataset with noisy labels. a and d are medical images. b and e are the ground truth (clean labels). c and f are the noisy labels. In masks, LV is labeled yellow, RV is labeled green, and Myo is labeled red

For LA dataset, the distribution of the IoU between the ground truth and the noisy label for different noisy ratios

LA dataset with noisy labels. a and d are medical images. b and e are the ground truth (clean labels). c and f are the noisy labels. The parts labeled in red are the segmented organs

3D Left atrium (LA) dataset. The LA dataset contains 100 subjects, which are composed of 3D gadolinium enhanced magnetic resonance images and their corresponding precise labels. The spatial resolution of the 3D image is 0.625 \(\times \) 0.625 \(\times \) 0.625 mm \(^{3}\) . Consistent with [ 56 ], we use 80 samples as the training set and 20 samples as the test set.

Noisy label generation of LA. The LA dataset contains labels for only one object class in addition to the background. To mimic realistic labeling inaccuracies within this dataset, we emulated the methodology delineated in [ 15 ], whereby the binary masks undergo stochastic morphological transformations such as erosion and dilation within a pixel range spectrum of 3 to 15, thusly engendering synthetic label noise. Since the LA dataset contains few objects that need to be segmented, we deliberately increase the ratio of samples with noisy labels. Consequently, the distribution of IoU between the noisy labels and clean labels is shown in Fig.  5 . The examples of original images, original labels and noisy labels in LA dataset is shown in Fig.  6 .

Implementation and evaluation metrics. Our experimental framework was executed using a single Nvidia 3090 GPU operating under Ubuntu 22.04 LTS. The development environment was structured utilizing Python 3.6, complemented by PyTorch 1.11.0 for constructing our deep learning models. For ACDC and LA datasets, we employed 2D Unet and 3D Unet architectures as our backbone networks respectively. For the ACDC dataset, we conducted training for 100 epochs, while for the LA dataset, the number of training epochs was set as 400, and we set batch size as 4. We initialized the learning rate at 0.01 and engaged the Stochastic Gradient Descent (SGD) optimizer for parameter updates.

In our study, we have employed standard evaluation metrics that are widely recognized in the domain of medical image segmentation. These include the Dice similarity coefficient, the Jaccard (JAC), and the 95% Hausdorff distance (95HD), providing a comprehensive assessment of segmentation accuracy and similarity to ground truth.

Compared method. To validate the robustness and effectiveness of our proposed method of noisy label learning, we benchmarked its performance against state-of-the-art methods:

GCE [ 36 ], a generalized cross entropy loss function is proposed for learning noisy labels.

Co-teaching [ 10 ], the two networks undergo simultaneous training, operating under a cooperative learning paradigm within mini-batch processing. Specifically, each network is responsible for identifying and selecting clean samples. These clean samples are then presented to the other network for learning.

TriNet [ 11 ], to select clean samples and learn more accurately, an additional network is added to the architecture of Co-teaching in this method. This configuration allows the networks to alternate roles, where each takes turns functioning as a ‘teacher’ to guide the learning process of the other networks.

2SRnT [ 51 ], There are also two models in this method. The teacher model is trained on the original dataset with noisy labels. The trained teacher model corrects the noise in the original dataset. The student model is trained on the corrected labels for the next step.

ADELE [ 13 ], according to the characteristics of network learning, this method makes the network correct the noise labels while being trained.

RMD [ 12 ], an algorithm in which two models perform complementary Co-training. A distillation module is designed to refine high-quality knowledge and enhance the use of reliable knowledge in this method.

Experiments on ACDC dataset

Comparison of segmentation results of seven model on the same image. All seven models were trained on the dataset contained noisy labels with a noisy ratio of 0.8

To substantiate the efficacy of our proposed meth, we have contrasted the performance of our model with the aforementioned model by training them on three ACDC datasets with different noisy ratios. Figure  7 illustrates the results inferred from models trained on the dataset with a noisy ratio of 0.8. The arrangement of Fig.  7 is as follows. The first column presents the images from the test dataset; the second column exhibits the true labels. Subsequently, columns 3 to 7 confer the prediction results from seven different models. Similarly to Fig.  4 , within the mask, the background is denoted by gray, and the LV, RV, and Myo components are represented by yellow, green, and red, respectively. Observationally, our proposed method’s prediction mask demonstrates a higher degree of resemblance to the true label. Evidently, it exhibits an enhanced capability to more accurately segment each section of the heart depicted in the figure.

A comprehensive comparison of evaluation metrics across our proposed approach and other methods are shown in Table  1 . We present results of four different noisy ratios (r = 0.2, 0.4, 0.6, 0.8). To enhance the clarity of the results, we have highlighted the most superior values in each set in bold and underlined those that are second best. It is discernible that the performance distinction among the various methods is relatively minute when applied to the group with a lower noisy ratio of 0.4. However, the disparity in effectiveness becomes more evident in the group with a significantly higher noisy ratio of 0.8. In this case, our proposed method demonstrates a sensational improvement, enhancing both the Dice and JAC metrics by an average of approximately 2.5%. Consequently, this implies that our methodology exhibits exceptional performance, even the training labels with high noisy ratio.

Experiments on LA dataset

Comparison of segmentation results of seven model on the same image. All seven models were trained on the LA dataset contained noisy labels with a noisy ratio of 0.9

Analogously, we train our model along with other existing models on three distinct LA datasets infused with different noisy ratios. The results yielded from the training on the dataset characterized by a considerably high noisy ratio of 0.9 are shown in Fig.  8 . To improve the comprehensibility of the results, we overlay the masks with the images.

Echoing the arrangement in Fig.  7 , Fig.  8 presents, from left to right, the images, the ground truth, and the inference results corresponding to seven models. We can see that when the segmentation of the target region is small, some methods have poor performance. Indeed, in severe instances, some methods fail to recognize the target region at all. However, our proposed method continues to yield commendable results, demonstrating a formidable ability to accurately segment even in circumstances where the target area is small or the boundary is intricate.

Furthermore, when our proposed method compares with other methods on the LA dataset, the evaluation metrics, as shown in Table  2 . The proposed method notably outperforms the others, especially in cases of high noisy ratios. For the performance indicator 95HD, with a noisy ratio as high as 0.9, our method successfully reduces the metric to 13.15 mm.

Ablation studies

In affirmation of the efficacy of our proposed method’s components, we have performed a series of ablation experiments. These trials were conducted on two distinct datasets—the ACDC dataset with a noisy ratio of 0.6 and the LA dataset with a noisy ratio of 0.9. The results of these experiments are consolidated in Table  3 .

In the first row, we observe performance of the baseline model. Following which, the second row of results elucidates the effects of incorporating Label Correction (LC) during the training phase for one network. LC is implemented by a single network, corresponding to the third column, here the label correction is guided by the student model (SG). In the third line, the model also uses Teacher model Guided (TG) in addition to label correction during training. Finally, the fourth row represents the introduction of Feature Perturbation (FP) to the student model. This set of experiments serves as a robust check to validate the effectiveness of individual components contributing to our proposed method.

It is worth noting that the second and third rows in Table  3 represent the results of experiments using a single model and a teacher model to guide the correction of noisy labels, respectively. In our proposed method, the teacher model solely focuses on correcting noisy labels and does not partake in model updates. Therefore, noisy label correction guided by a single model is equivalent to correction guided by the student model.

The results of using student model and teacher model for label correction of ACDC dataset with a noisy ratio of 0.6. The “SG” represents the student model guided, and “TG” represents the teacher model guided

The results of using student model and teacher model for label correction of LA dataset with a noisy ratio of 0.9. The “SG” represents the student model guided, and “TG” represents the teacher model guided

Since the ACDC dataset contains four target organs requiring segmentation, mere label correction proves challenging with low accuracy, yielding minimal enhancement in model performance. With the integration of the TG component, substantial improvements are observed across all metrics, validating the efficacy of TG. The Fig.  9 shows that, when the image contours are clear, the results are close to the ground truth regardless of whether the noisy label correction is guided by the student model or the teacher model. However, when the image is not clear or interference occurs around the target (the second and third rows), the results obtained using the student model for label correction have larger errors. And the LA dataset comprises one class, thus, correcting noisy labels during training markedly enhances model performance. It can be seen in Fig.  10 that the results of SG and TG are similar. Our analysis reveals a significant hike in the model’s performance on datasets containing multiple class labels, when TG is introduced.

Experiments were carried out to determine the influence of the hyperparameter g , which governs the commencement timing of correcting the noisy labels. The outcomes of these tests are presented in Table  4 . A discernible degradation in performance was observed when g was set at 0.6 and 0.7. In contrast, the performance variation was relatively insignificant when g was adjusted to 0.8, 0.9, and 0.99.

Moreover, we conducted experiments on the hyperparameter \(\phi _{max}\) within \(\gamma (t)\) , which controls the proportion of the loss function. The corresponding results are shown in Table  5 . According to the tabulated data, the model exhibits optimal performance when \(\phi _{max}\) is set to 0.8.

In this paper, we propose a novel method aimed at addressing the issue of noisy label learning within the field of medical image segmentation. Our method incorporates both a student model and a teacher model, enabling learning from high-quality and low-quality datasets and yielding exceptional outcomes. The teacher model adaptively rectifies the noisy labels within the dataset during the training phase. Meanwhile, the student model learns from the labels of the current dataset and the denoised labels. Alongside, feature perturbations are incorporated into the student model to bolster robustness. The results from our experiments indicate that our method is highly effective. When applied to the ACDC dataset with a noisy ratio of 0.8, the mean Dice is enhanced by 2.58%.

Data Availability

The data used to support the findings of this study are included within the paper.

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Liu, S., Zou, M., Liu, N. et al. A teacher-guided early-learning method for medical image segmentation from noisy labels. Complex Intell. Syst. (2024). https://doi.org/10.1007/s40747-024-01574-1

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Published on 13.8.2024 in Vol 26 (2024)

Effect of a Mobile Health–Based Remote Interaction Management Intervention on the Quality of Life and Self-Management Behavior of Patients With Low Anterior Resection Syndrome: Randomized Controlled Trial

Authors of this article:

Original Paper

• Peng Zhou 1, 2 * , MNS   ; 
• Hui Li 3 * , MSN   ; 
• Xueying Pang 4 * , MSN   ; 
• Ting Wang 4 , MSN   ; 
• Yan Wang 2 , MSN   ; 
• Hongye He 4 , MSN   ; 
• Dongmei Zhuang 2 , MSN   ; 
• Furong Zhu 2 , MNS   ; 
• Rui Zhu 1 , MSN   ; 
• Shaohua Hu 1 , PhD  

1 Department of Nursing, the First Affiliated Hospital of Anhui Medical University, Hefei, China

2 School of Nursing, Anhui Medical University, Hefei, China

3 College of Traditional Chinese Medicine, Bozhou University, Bozhou, China

4 Department of Gastrointestinal Surgery, the First Affiliated Hospital of Anhui Medical University, Hefei, China

*these authors contributed equally

Corresponding Author:

Shaohua Hu, PhD

Department of Nursing

the First Affiliated Hospital of Anhui Medical University

218 Jixi Road

Hefei, 230009

Phone: 86 62922005

Email: [email protected]

Background: People who undergo sphincter-preserving surgery have high rates of anorectal functional disturbances, known as low anterior resection syndrome (LARS). LARS negatively affects patients’ quality of life (QoL) and increases their need for self-management behaviors. Therefore, approaches to enhance self-management behavior and QoL are vital.

Objective: This study aims to assess the effectiveness of a remote digital management intervention designed to enhance the QoL and self-management behavior of patients with LARS.

Methods: From July 2022 to May 2023, we conducted a single-blinded randomized controlled trial and recruited 120 patients with LARS in a tertiary hospital in Hefei, China. All patients were randomly assigned to the intervention group (using the “e-bowel safety” applet and monthly motivational interviewing) or the control group (usual care and an information booklet). Our team provided a 3-month intervention and followed up with all patients for an additional 3 months. The primary outcome was patient QoL measured using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30. The secondary outcomes were evaluated using the Bowel Symptoms Self-Management Behaviors Questionnaire, LARS score, and Perceived Social Support Scale. Data collection occurred at study enrollment, the end of the 3-month intervention, and the 3-month follow-up. Generalized estimating equations were used to analyze changes in all outcome variables.

Results: In the end, 111 patients completed the study. In the intervention group, 5 patients withdrew; 4 patients withdrew in the control group. Patients in the intervention group had significantly larger improvements in the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 total score (mean difference 11.51; 95% CI 10.68-12.35; Cohen d =1.73) and Bowel Symptoms Self-Management Behaviors Questionnaire total score (mean difference 8.80; 95% CI 8.28-9.32; Cohen d =1.94) than those in the control group. This improvement effect remained stable at 3-month follow-up (mean difference 14.47; 95% CI 13.65-15.30; Cohen d =1.58 and mean difference 8.85; 95% CI 8.25-9.42; Cohen d =2.23). The LARS score total score had significantly larger decreases after intervention (mean difference –3.28; 95% CI –4.03 to –2.54; Cohen d =–0.39) and at 3-month follow-up (mean difference –6.69; 95% CI –7.45 to –5.93; Cohen d =–0.69). The Perceived Social Support Scale total score had significantly larger improvements after intervention (mean difference 0.47; 95% CI 0.22-0.71; Cohen d =1.81).

Conclusions: Our preliminary findings suggest that the mobile health–based remote interaction management intervention significantly enhanced the self-management behaviors and QoL of patients with LARS, and the effect was sustained. Mobile health–based remote interventions become an effective method to improve health outcomes for many patients with LARS.

Trial Registration: Chinese Clinical Trial Registry ChiCTR2200061317; https://tinyurl.com/tmmvpq3

Introduction

The Global Cancer Statistics 2020 showed that colorectal cancer ranks third in incidence of malignant tumors and second in cause of death worldwide [ 1 ]. Colorectal cancer incidence is also on the rise in China, with rectal cancer accounting for 60% of cases and middle and lower rectal cancers being the most common [ 2 ]. With the advancement of medical technology, optimal management of middle and lower rectal cancers increasingly favors sphincter-preserving surgery (SPS) [ 3 ]. This operation preserves anal function and avoids the inconvenience and pressure caused by permanent colostomy [ 4 ]. However, 70%-90% of patients after SPS struggle with long-term anorectal functional disturbances called low anterior resection syndrome (LARS) [ 5 , 6 ].

The presence of LARS has a severe adverse effect on the quality of life (QoL) of patients [ 7 ]. Postoperative LARS induces a spectrum of adverse physical and psychological effects in patients; for example, up to 50% of patients with LARS report toilet dependence during rehabilitation [ 8 , 9 ], 36% of patients experience pain, and approximately 13% of patients report high psychological distress [ 10 , 11 ]. Furthermore, LARS can restrict a patient’s social life, leading to further impact on their QoL [ 12 ]. Recently, longitudinal studies have found that patients’ QoL is still affected by LARS even 15 years after surgery [ 13 ]. Research has shown that patients can improve their QoL through methods, such as pelvic floor muscle exercises and dietary adjustments during home care; however, the effectiveness of these methods is limited by patients’ lack of knowledge of LARS and rehabilitation guidance [ 14 , 15 ].

Owing to the frequent occurrence of LARS in patients post discharge, patients must have a high level of self-management behavior [ 16 ]. However, in China, the majority of patients have a passive response to LARS, and their self-management behavior is at a low level [ 17 ]. Enhancing self-management awareness and providing information on supportive care can improve the self-management behavior of patients with LARS [ 18 ]. Research has demonstrated that motivational interviewing (MI) enhances self-management awareness and supports behavioral change [ 19 ].

Therefore, to improve patients’ QoL and self-management behaviors, providing supportive care information to patients is crucial. A qualitative exploration of patients with LARS’s perspectives on information needs revealed that timely symptom management measures are critical during home-based rehabilitation [ 20 ]. However, it is difficult to maintain continuity and instantaneity with existing management measures [ 21 , 22 ]. Owing to current advances in mobile technology, mobile health (mHealth) has been widely considered a means of patient health management, which can improve the effects of symptoms and assist patients in timely access to the required information [ 23 , 24 ].

To date, remote follow-up tools for patients with LARS have yielded promising results [ 25 ]. For patients with LARS, mHealth-based remote interventions may become an effective method to assist them in improving symptoms. However, mHealth intervention measures constructed for patients with LARS are rare. Most studies have only completed the development and pilot research of remote intervention programs, leading to insufficient data on the effectiveness of remote interventions in improving patient health outcomes [ 26 , 27 ]. WeChat (Tencent Corp) is China’s most frequently used instant messaging and social media application [ 28 ]. Evidence suggests that WeChat-based mHealth interventions effectively improve health outcomes in various health conditions [ 29 , 30 ].

This study aimed to assess the effectiveness of a remote digital management intervention designed for patients with LARS. The effectiveness of the intervention measure is determined by improvement in QoL, self-management behaviors, gastrointestinal symptoms, and social support. We hypothesized that the remote digital management intervention can effectively improve the health outcomes of patients with LARS.

Study Design

This study was conducted from July 15, 2022, to March 15, 2023, in Hefei, China. Our team provided a 3-month intervention and followed up with all patients for an additional 3 months. The intervention group used the “e-bowel safety” applet and received monthly MI. The control group received the usual care and was provided with a handbook containing information related to LARS. The CONSORT (Consolidated Standards of Reporting Trials) checklist is in Multimedia Appendix 1 .

Ethical Considerations

This randomized controlled trial (RCT) was approved by the ethics committee of the First Affiliated Hospital of Anhui Medical University (PJ2022-07-53) and registered on the Chinese Clinical Trial Registry (ChiCTR2200061317). All data were identified with a code number to ensure the confidentiality of the subjects’ data. No compensation was provided to participants.

Participants

The patients were recruited from a tertiary hospital in Hefei, Anhui Province, China. Patients were eligible to participate in our study if they met the following criteria: age older than 18 years, a diagnosis of rectal cancer, underwent SPS, LARS scores ≥21, ostomy closure surgery performed at least 3 months prior, the ability to read and write text, and proficiency in using WeChat. Patients with chronic gastrointestinal conditions, prior or current mental health disorders, cognitive impairments, communication disorders, or those who have participated in other clinical studies are ineligible for participation in this research. When patients meeting the recruitment criteria appeared in the hospital database, the system sent recruitment information to these patients with the approval of doctors not directly involved in the research design.

In this study, the sample size was determined based on the QoL. Previous research has shown that the QoL for patients with rectal cancer is 77±19 [ 31 ]. In an RCT using the EORTC QLQ-C30, a difference of 10 points is considered clinically significant [ 32 ]. With a two-sided test level of 0.05 and 80% test efficacy, each group requires a sample size of 45. Accounting for a 20% dropout rate, 112 patients are needed.

Intervention

Our previous study provided a comprehensive description of the intervention protocol [ 33 ]. The patients in the intervention group used the “e-bowel safety” applet for 3 months. They were required to check in on the applet daily and record their daily gastrointestinal symptoms. Our “e-bowel safety” applet comprises 4 main sections: a rehabilitation plan, LARS knowledge, web-based consultation, and patient stories. The rehabilitation plan module involves the collaborative development of home dietary and exercise plans by patients and researchers. The applet features intelligent reminders to monitor daily plan completion and provide prompts. After completing the rehabilitation plan, patients must fill out a daily health diary, and researchers dynamically adjust the rehabilitation plan based on patients’ feedback and physical condition. The LARS knowledge module offers evidence-based information on LARS and symptom management strategies. The web-based consultation module provides patients with an opportunity to interact with health care professionals, offering personalized guidance and feedback. The patient stories module allows patients to share symptom management experiences or engage with other patients, with all published content subject to researcher approval. Additionally, an incentive system has been designed to encourage participation. For instance, patients earn points by sharing personal stories or comments, which can later be exchanged for rewards after accumulating a certain number of points.

Moreover, our team members conducted monthly MIs with patients. MIs were led by 4 researchers with expertise in health coaching and disease management, including 1 clinical psychologist (Shangxin Zhang) and 3 registered nurses (TW, HH, and Ling Fang). The researchers engage with patients via WeChat for 30-60 minutes per call. The aim of MIs is to assist patients in setting rehabilitation goals, reinforcing self-management awareness, and promoting health behavior changes. The content of MIs is based on the interview guide determined by the research team, which guides the conversation from the initial session to explore the participant’s motivation to identify the facilitating factors and barriers to achieving their health goals. The interview guide is outlined in Multimedia Appendix 2 .

Patients in the control group received the usual care and were provided with a handbook containing information related to LARS. At the same time, our team members followed up with patients, using the same timing and frequency as the MI intervention group.

Randomization and Masking

This study was a single-blind, two-arm RCT. After obtaining consent from eligible patients, assistants who were not involved in the study randomly assigned them to the intervention and control groups at a 1:1 ratio. The randomization process was performed by the assistants and anonymized envelopes were used with block randomization, including block sizes randomly varying between 4 (2:2) and 6 (3:3). The research assistants (Ping Ni and Ai Wang) who collected the data were unaware of the patient assignments throughout the study. Patients used the QR codes provided by the research team to access the “e-bowel safety” applet, effectively reducing contamination between the 2 groups. Patients were blinded to their group assignments throughout the entire research process.

Quality Control and Participant Retention

Several strategies were used to ensure quality control and participant retention. Our “e-bowel safety” applet can monitor patients’ plan execution and provide reminders, which ensures the daily plans are followed strictly by patients. Before the formal intervention, we conducted a pilot experiment and gathered participant feedback to enhance our plan. The specific results are included in Multimedia Appendix 3 . Furthermore, patients received consistent guidance from our research assistants (Ping Ni and Ai Wang) when they had questions about the questionnaire content. Before the start of the study, all research assistants must undergo training and assessment on the use of all questionnaires by research team members. Only research assistants who pass the assessment can participate in data collection. Additionally, team members regularly check the progress of research assistants’ work to ensure that they are following the questionnaire collection process, identifying issues promptly, and making corrections.

Outcome Measures

The patients’ demographic and clinical information were obtained from the hospital database. Data were collected from patients using scales for their QoL, social support, self-management behaviors, and LARS scores at different time periods (0, 3, and 6 months). The research assistants (Ping Ni and Ai Wang) who collected the data assisted patients in completing questionnaires over the phone or through direct personal interaction.

Primary Outcome: QoL

The EORTC-QLQ-C30 (European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30) was used to measure QoL. This questionnaire comprises 30 items divided into 15 dimensions, including 1 dimension for QoL, 5 dimensions for functionality, 3 dimensions for symptoms, and 6 dimensions for additional symptoms. All dimension scores were linearly transformed to a scale of 0-100 points. Elevated scores on the 5 functionality dimensions and the QoL dimension were linked to improved functional status, whereas the reverse pattern was observed for the symptom dimensions and additional symptom dimensions. The Cronbach α coefficient ranged from 0.764 to 0.809 [ 34 ].

Secondary Outcome

Self-management.

The self-management behavior of patients was assessed by the Bowel Symptoms Self-Management Behaviors Questionnaire (BSSBQ). This questionnaire comprises 24 items divided into 5 functional scales, with each item scored on a scale of 0 (never) to 7 (always). Higher scores indicate better bowel symptom self-management behavior. The Cronbach α coefficient was 0.81 [ 17 ].

Bowel Function

The LARS score consists of 5 items, with a total score ranging from 0 to 42. Patients’ gastrointestinal symptoms are classified into no LARS, minor LARS, and major LARS based on the total score. The LARS score is a validated instrument for assessing bowel symptoms. The Cronbach α coefficient was 0.767 [ 35 ].

Social Support

The Perceived Social Support Scale (PSSS) consists of 12 items, with each item scored on a scale of 1 (extreme disagreement) to 7 (strong consent). The total scores ranged from 12 to 84. The higher the score, the stronger the perceived social support by the patient. This scale is widely used to assess the level of social support among patients in China. The Cronbach α coefficient of this scale was 0.899 [ 36 ].

Feasibility

The feasibility of intervention was assessed through the completion status of MI sessions and the adherence to health diary entries. The 3-month intervention corresponds to 3 MI sessions and 84 days of health diary entries.

Statistical Methods

All data were analyzed using SPSS Statistics (version 23.0; IBM Corp). An intention-to-treat analysis was performed in this study. We used the last observed values of the patients to replace missing data. Chi-square analysis was used to analyze the remaining demographic characteristics, and a 2-tailed independent sample t test was used to analyze the age and tumor height. Descriptive data were computed, including means with SD, medians with ranges, and frequencies with proportions where appropriate. The statistical significance was established at P <.05 (2-tailed test). Generalized estimating equations were used to analyze changes in QoL, self-management behaviors, LARS, and social support scores at different time points. The calculation of effect sizes was performed using Cohen d for the mean differences at various time periods.

Participant Characteristics

Initially, 60 patients were recruited in the control and intervention groups. During the study, 9 patients dropped out (dropout rate 7.5%). In the intervention group, 5 patients withdrew from the study, including 2 patients who received a reostomy because of an anastomotic fistula and 3 patients whose condition worsened. In the control group, 4 patients dropped out, including 2 patients whose condition worsened and 2 patients who refused to continue the intervention because of the side effects of chemotherapy. No statistically significant differences were observed between the patients who dropped out and those who completed all evaluations ( P =.17). Figure 1 shows the CONSORT flowchart of this study. Table 1 demonstrates no statistically significant differences in the demographics and clinical information between the control and intervention groups at baseline.

a LAR: low anterior resection.

b TaTME: transanal total mesorectal excision.

c EORTC-QLQ-C30: European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30.

d BSSBQ: Bowel Symptoms Self-Management Behaviors Questionnaire.

e LARS: Low anterior resection syndrome.

f PSSS: Perceived Social Support Scale.

Main Evaluation Indexes

Table 2 shows that the patients’ QoL improved for both groups. Patients in the intervention group demonstrated greater improvements in the EORTC-QLQ-C30 total score than those in the control group after intervention (mean difference 11.51; 95% CI 10.68-12.35; Cohen d =1.73). Furthermore, this improvement effect remained stable at 3-month follow-up (mean difference 14.47; 95% CI 13.65-15.30; Cohen d =1.58). Table 3 shows that the EORTC-QLQ-C30 total score in both groups exhibited a trend of change over the 6-month period ( P <.001). Differences were observed between the 2 groups and the interaction between group and time. A subgroup analysis was conducted on patients receiving preoperative chemotherapy versus postoperative chemotherapy. Among the 49 patients in the intervention group and 51 in the control group undergoing postoperative chemotherapy, a nominally significant improvement in the change from baseline in the EORTC-QLQ-C30 total score at 3 months was observed compared to the control group (difference of 4.42; P <.001). However, this effect was not seen in patients receiving preoperative chemotherapy. The specific results are included in Multimedia Appendix 4 .

a GEE: Generalized estimating equations.

b Difference in mean change from baseline to endpoint between the groups.

d Baseline.

e N/A: Not applicable.

f After the intervention.

g 3-month follow-up.

h BSSBQ: Bowel Symptoms Self-Management Behavior Questionnaire.

i LARS: Low anterior resection syndrome score.

j PSSS: Perceived Social Support Scale.

a EORTC-QLQ-C30: European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30.

b BSSBQ: Bowel Symptoms Self-Management Behaviors Questionnaire.

c LARS: Low anterior resection syndrome.

d PSSS: Perceived Social Support Scale.

Secondary Evaluation Indexes

Table 2 shows that the patients’ self-management behavior was enhanced for both groups. The BSSBQ total score had significantly larger improvements after intervention (mean difference 8.80; 95% CI 8.28-9.32; Cohen d =1.94) and at 3-month follow-up (mean difference 8.85; 95% CI 8.25-9.42; Cohen d =2.23) between groups. The BSSBQ total score showed statistically significant time effects ( P <.001; Table 3 ).

The LARS score total score had significantly larger decreases after intervention (mean difference –3.28; 95% CI –4.03 to –2.54; Cohen d =–0.39) and at 3-month follow-up (mean difference –6.69; 95% CI –7.45 to –5.93; Cohen d =–0.69). Table 3 shows that the LARS score total score in both groups exhibited a trend of change over the 6-month period. The intergroup effect exhibits homogeneity ( P =.05).

The PSSS total score had significantly larger improvements after intervention (mean difference 0.47; 95% CI 0.22-0.71; Cohen d =1.81); however, the improvement in this effect did not persist at 3-month follow-up (mean difference 0.23; 95% CI –0.20 to 0.45; P =.07; Table 2 ). Table 3 shows that the PSSS total score in both groups exhibited a trend of change over the 6-month period.

Among the 55 patients who completed the intervention, 45 patients completed 3 MI sessions on time, 7 patients postponed 1 MI session because of scheduling conflicts, and 3 patients only completed 2 MI sessions. The mean number of attended MI sessions was 2.95 (SD 0.23). Additionally, 40 patients completed 84 health diary entries, while the remaining 11 patients did not submit completed entries or fulfill the required entries. The mean number of days of health diary entries was 82.87 (SD 3.15). We invited patients from the intervention group to complete a survey to evaluate their perceptions of the intervention's usability. In the end, 49 people completed the survey. The specific results are included in Appendix 5.

Principal Findings

To the best of our knowledge, the “e-bowel safety” applet is the first mobile app developed for patients with LARS in China. This study offers a valuable reference point for future initiatives in mHealth interventions for patients with LARS. A mHealth-based intervention was found to be feasible and effective in helping patients with LARS relieve bowel dysfunction, improve their self-management behavior, and improve their QoL compared to usual care.

This study found that the EORTC-QLQ-C30 total score of the intervention group increased significantly more than that of the control group after the intervention, indicating that the mHealth-based remote interaction could improve the QoL of patients with LARS. These results can be attributed to multiple factors. First, uncontrollable changes in intestinal function, concerns about prognosis, and fear of the future make patients with LARS feel uncertain [ 37 ]. A sense of uncertainty influences a patient’s QoL [ 38 ]. Patients using the “e-bowel safety” applet can provide timely feedback on their problems to the medical staff and obtain solutions, which can effectively reduce the uncertainty of patients during home rehabilitation. Second, decreased bowel dysfunction severity positively affected the QoL [ 39 ]. Third, peer support reportedly enhances cancer adaptation and QoL [ 40 ]. The patients’ stories module offers a channel for communication and emotional support among patients with LARS. In this section, patients can share their experiences related to disease management or self-management and receive responses from their peers through comments.

As expected, the BSSBQ total score in the intervention group after the intervention was significantly higher than that in the control group. The findings supported our hypothesis that health-based remote interaction can enhance the self-management behavior of patients with LARS. After the intervention, the results of enhanced self-management behavior were consistent with a previous face-to-face 6-month self-management program study for LARS, which may indicate that mHealth-based remote interaction may yield intervention effects on self-management behavior similar to those observed in face-to-face interventions [ 41 ]. However, a more significant effect was observed at 3-month follow-up. This may be because monthly motivational interviews help patients adopt positive health behaviors and improve their self-management awareness [ 42 ]. Moreover, current web-based self-management information on LARS is overly intricate for patients, and the information fails to meet the patient’s needs [ 43 ]. The strength of our “e-bowel safety” applet is the credibility of the information provided and medical consultation from experts, which can meet the information needs of patients. Finally, our team members created an individualized self-management plan for each participant in the intervention group and reminded them to follow the plans on the applet, which ensured that the patients developed good habits.

Consistent with previous studies [ 41 ], this study found that the intervention group demonstrated a more significant decline in the LARS score than the control group. The LARS score also showed significant time effects, indicating that the patient’s bowel dysfunction changed significantly during the 6-month period. This may be because our team members guided patients in rehabilitation exercises and diet adjustments, which have been proven effective in improving bowel dysfunction [ 44 - 46 ]. Meanwhile, the severity of bowel dysfunction decreased over time [ 13 ].

Unlike those of previous studies, our findings indicated that mHealth-based remote interaction management intervention could improve the social support levels in the short term; however, sustaining a stable long-term effect on social support was not realized [ 47 ]. The patients in the study might have used the “e-bowel safety” applet only for 3 months, and the impact of the intervention on social support may not yield a residual advantage at 3-month follow-up. Furthermore, most patients’ physical and social functions gradually stabilized at 6 months. Our “e-bowel safety” applet focuses on intensive support for symptom management and lacks support knowledge for patients when symptoms plateau, which should be refined in future studies to achieve long-term effects.

In this study, MI was used to stimulate behavioral change and maintenance. The dual intervention of mHealth and MI promotes effective engagement and motivation for health behavior changes. Nearly all the patients (55/60) successfully completed the 3-month intervention and the follow-up during the intervention process, signifying that the mHealth-based remote interaction management intervention is feasible and acceptable. In addition, none of the patients in the intervention group experienced adverse consequences caused by the intervention, indicating that the intervention was safe.

Limitations

This study has some limitations. First, this study enrolled patients from a tertiary hospital in China, which restricts the generalizability of our results. In the future, we will recruit patients from more hospitals to confirm our research findings. Second, patients were subjected to a limited 3-month follow-up period, thereby restricting our assessment of the enduring effects of the mHealth-based remote interaction management intervention on self-management behavior and QoL. Finally, patients were required to use WeChat and smartphones, which presents the potential for selection bias.

Conclusions

The mHealth-based remote interaction management intervention effectively enhanced the self-management behavior and QoL of patients with LARS, and the impact remained consistent during the 3-month follow-up. Bowel dysfunction also significantly improved throughout the entire research process. This study suggests that mHealth intervention could provide an effective and new option for many patients with LARS. Multicenter studies are necessary to establish the generalizability and effectiveness of these interventions.

Acknowledgments

This work was supported by the 2021 Anhui Higher Education Institutions Provincial Quality Engineering Project (grant 2021jyxm0718) and the Scientific Research and Cultivation project of the School of Nursing, Anhui Medical University (grant hlqm12023055).

Conflicts of Interest

None declared.

CONSORT-EHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile HEalth Applications and onLine TeleHealth) checklist (version 1.6.1).

The interview guide of motivational interviewing.

Results of pilot experiment.

The results of subgroup analysis.

Comments and attitudes towards intervention of intervention group.

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• Moon J, Monton O, Smith A, Garfinkle R, Zhao K, Zelkowitz P, et al. Interactive online informational and peer support application for patients with low anterior resection syndrome: patient survey and protocol for a multicentre randomized controlled trial. Colorectal Dis. 2021;23(5):1248-1257. [ CrossRef ] [ Medline ]
• Olivia M, Allister S, Jeongyoon M, Marie D, Richard G, Carol-Ann V, et al. An online educational and supportive care application for rectal cancer survivors with low anterior resection syndrome: a mixed methods pilot study. Colorectal Dis. 2023;25(9):1812-1820. [ CrossRef ] [ Medline ]
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• Bao Y, Wang C, Xu H, Lai Y, Yan Y, Ma Y, et al. Effects of an mHealth intervention for pulmonary tuberculosis self-management based on the integrated theory of health behavior change: randomized controlled trial. JMIR Public Health Surveill. 2022;8(7):e34277. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Guo Y, Hong YA, Cai W, Li L, Hao Y, Qiao J, et al. Effect of a WeChat-based intervention (Run4Love) on depressive symptoms among people living with HIV in China: a randomized controlled trial. J Med Internet Res. 2020;22(2):e16715. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Battersby NJ, Juul T, Christensen P, Janjua AZ, Branagan G, Emmertsen KJ, et al. Predicting the risk of bowel-related quality-of-life impairment after restorative resection for rectal cancer: a multicenter cross-sectional study. Dis Colon Rectum. 2016;59(4):270-280. [ CrossRef ] [ Medline ]
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• Emmertsen K, Laurberg S, Rectal Cancer Function Study Group. Impact of bowel dysfunction on quality of life after sphincter-preserving resection for rectal cancer. Br J Surg. 2013;100(10):1377-1387. [ CrossRef ] [ Medline ]
• Brodar KE, Carlisle V, Tang PY, Fisher EB. Identification and characterization of peer support for cancer prevention and care: a practice review. J Cancer Educ. 2022;37(3):645-654. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Li C, Li Z, Lu Q, Zhou Y, Qin X, Wu A, et al. The effectiveness of a self-management program of bowel dysfunction in patients with mid and low rectal cancer after sphincter-preserving surgery: a pilot randomized controlled trial. Cancer Nurs. 2023;46(1):67-76. [ CrossRef ] [ Medline ]
• Seven M, Reid A, Abban S, Madziar C, Faro JM. Motivational interviewing interventions aiming to improve health behaviors among cancer survivors: a systematic scoping review. J Cancer Surviv. 2023;17(3):795-804. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Lightner A. Assessing the readability, quality and accuracy of online health information for patients with low anterior resection syndrome following surgery for rectal cancer, by Garfinkle et al. Colorectal Dis. 2019;21(5):500. [ CrossRef ]
• Yuanyuan W, Shiyin H, Lei H, Ding D. Pelvic floor muscle exercises alleviate symptoms and improve mental health and rectal function in patients with low anterior resection syndrome. Front Oncol. 2023;13:1168807. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Sun V, Crane TE, Slack SD, Yung A, Wright S, Sentovich S, et al. Rationale, development, and design of the altering intake, managing symptoms (AIMS) dietary intervention for bowel dysfunction in rectal cancer survivors. Contemp Clin Trials. 2018;68:61-66. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Sun V, Crane TE, Freylersythe S, Slack SD, Yung A, Krouse RS, et al. Altering intake and managing symptoms: feasibility of a diet modification intervention for post-treatment bowel dysfunction in rectal cancer. Clin J Oncol Nurs. 2022;26(3):283-292. [ CrossRef ] [ Medline ]
• Zhu J, Ebert L, Liu X, Wei D, Chan SW. Mobile breast cancer e-Support program for Chinese women with breast cancer undergoing chemotherapy (part 2): multicenter randomized controlled trial. JMIR Mhealth Uhealth. 2018;6(4):e104. [ FREE Full text ] [ CrossRef ] [ Medline ]

Abbreviations

Edited by A Mavragani; submitted 24.10.23; peer-reviewed by V Sun, C Thomson; comments to author 13.03.24; revised version received 07.05.24; accepted 03.06.24; published 13.08.24.

©Peng Zhou, Hui Li, Xueying Pang, Ting Wang, Yan Wang, Hongye He, Dongmei Zhuang, Furong Zhu, Rui Zhu, Shaohua Hu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 13.08.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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What 'Project 2025' Would Mean for Health and Healthcare

George Lundberg, MD

Authors and Disclosures

Disclosure: George D. Lundberg, MD, has disclosed no relevant financial relationships.

The Heritage Foundation sponsored and developed Project 2025 for the explicit, stated purpose of building a conservative victory through policy, personnel, and training with a 180-day game plan after a sympathetic new President of the United States takes office. To date, Project 2025 has not been formally endorsed by any presidential campaign.

More than 100 conservative organizations are said to be participating. More than 400 conservative scholars and experts have collaborated in authorship of the mandate's 40 chapters. Chapter 14 of the "Mandate for Leadership" is an exhaustive proposed overhaul of the Department of Health and Human Services (HHS), one of the major existing arms of the executive branch of the US government. 

The mandate's sweeping recommendations, if implemented, would impact the lives of all Americans and all healthcare workers, as outlined in the following excerpts. 

Healthcare-Related Excerpts From Project 2025

• "From the moment of conception, every human being possesses inherent dignity and worth, and our humanity does not depend on our age, stage of development, race, or abilities. The Secretary must ensure that all HHS programs and activities are rooted in a deep respect for innocent human life from day one until natural death: Abortion and euthanasia are not health care."
• "Unfortunately, family policies and programs under President Biden's HHS are fraught with agenda items focusing on 'LGBTQ+ equity,' subsidizing single motherhood, disincentivizing work, and penalizing marriage. These policies should be repealed and replaced by policies that support the formation of stable, married, nuclear families."
• "The next Administration should guard against the regulatory capture of our public health agencies by pharmaceutical companies, insurers, hospital conglomerates, and related economic interests that these agencies are meant to regulate. We must erect robust firewalls to mitigate these obvious financial conflicts of interest."
• "All National Institutes of Health, Centers for Disease Control and Prevention, and Food and Drug Administration regulators should be entirely free from private biopharmaceutical funding. In this realm, 'public–private partnerships' is a euphemism for agency capture, a thin veneer for corporatism. Funding for agencies and individual government researchers must come directly from the government with robust congressional oversight."
• "The CDC [Centers for Disease Control and Prevention] operates several programs related to vaccine safety including the Vaccine Adverse Event Reporting System (VAERS); Vaccine Safety Datalink (VSD); and Clinical Immunization Safety Assessment (CISA) Project. Those functions and their associated funding should be transferred to the FDA [Food and Drug Administration], which is responsible for post-market surveillance and evaluation of all other drugs and biological products."
• "Because liberal states have now become sanctuaries for abortion tourism, HHS should use every available tool, including the cutting of funds, to ensure that every state reports exactly how many abortions take place within its borders, at what gestational age of the child, for what reason, the mother's state of residence, and by what method. It should also ensure that statistics are separated by category: spontaneous miscarriage ; treatments that incidentally result in the death of a child (such as chemotherapy); stillbirths; and induced abortion. In addition, CDC should require monitoring and reporting for complications due to abortion and every instance of children being born alive after an abortion."
• "The CDC should immediately end its collection of data on gender identity , which legitimizes the unscientific notion that men can become women (and vice versa) and encourages the phenomenon of ever-multiplying subjective identities."
• "A test developed by a lab in accordance with the protocols developed by another lab (non-commercial sharing) currently constitutes a 'new' laboratory-developed test because the lab in which it will be used is different from the initial developing lab. To encourage interlaboratory collaboration and discourage duplicative test creation (and associated regulatory and logistical burdens), the FDA should introduce mechanisms through which laboratory-developed tests can easily be shared with other laboratories without the current regulatory burdens."
• "[FDA should] Reverse its approval of chemical abortion drugs because the politicized approval process was illegal from the start. The FDA failed to abide by its legal obligations to protect the health, safety, and welfare of girls and women."
• "[FDA should] Stop promoting or approving mail-order abortions in violation of long-standing federal laws that prohibit the mailing and interstate carriage of abortion drugs."
• "[HHS should] Promptly restore the ethics advisory committee to oversee abortion- derived fetal tissue research, and Congress should prohibit such research altogether."
• "[HHS should] End intramural research projects using tissue from aborted children within the NIH, which should end its human embryonic stem cell registry."
• "Under Francis Collins, NIH became so focused on the #MeToo movement that it refused to sponsor scientific conferences unless there were a certain number of women panelists, which violates federal civil rights law against sex discrimination. This quota practice should be ended, and the NIH Office of Equity, Diversity, and Inclusion, which pushes such unlawful actions, should be abolished."
• "Make Medicare Advantage [MA] the default enrollment option."
• "[Legislation reforming legacy (non-MA) Medicare should] Repeal harmful health policies enacted under the Obama and Biden Administrations such as the Medicare Shared Savings Program and Inflation Reduction Act."
• "…the next Administration should] Add work requirements and match Medicaid benefits to beneficiary needs. Because Medicaid serves a broad and diverse group of individuals, it should be flexible enough to accommodate different designs for different groups."
• "The No Surprises Act should scrap the dispute resolution process in favor of a truth-in-advertising approach that will protect consumers and free doctors, insurers, and arbiters from confused and conflicting standards for resolving disputes that the disputing parties can best resolve themselves."
• "Prohibit abortion travel funding. Providing funding for abortions increases the number of abortions and violates the conscience and religious freedom rights of Americans who object to subsidizing the taking of life."
• "Prohibit Planned Parenthood from receiving Medicaid funds. During the 2020–2021 reporting period, Planned Parenthood performed more than 383,000 abortions."
• "Protect faith-based grant recipients from religious liberty violations and maintain a biblically based, social science–reinforced definition of marriage and family. Social science reports that assess the objective outcomes for children raised in homes aside from a heterosexual, intact marriage are clear."
• "Allocate funding to strategy programs promoting father involvement or terminate parental rights quickly."
• "Eliminate the Head Start program."
• "Support palliative care. Physician-assisted suicide (PAS) is legal in 10 states and the District of Columbia. Legalizing PAS is a grave mistake that endangers the weak and vulnerable, corrupts the practice of medicine and the doctor–patient relationship, compromises the family and intergenerational commitments, and betrays human dignity and equality before the law."
• "Eliminate men's preventive services from the women's preventive services mandate. In December 2021, HRSA [Health Resources and Services Administration] updated its women's preventive services guidelines to include male condoms."
• "Prioritize funding for home-based childcare, not universal day care."
• " The Office of the Secretary should eliminate the HHS Reproductive Healthcare Access Task Force and install a pro-life task force to ensure that all of the department's divisions seek to use their authority to promote the life and health of women and their unborn children."
• "The ASH [Assistant Secretary for Health] and SG [Surgeon General] positions should be combined into one four-star position with the rank, responsibilities, and authority of the ASH retained but with the title of Surgeon General."
• "OCR [Office for Civil Rights] should withdraw its Health Insurance Portability and Accountability Act (HIPAA) guidance on abortion."

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Home / Research & Medical / Metabolic / Diagnosing and Managing Hyperinsulinemia in Horses  

Diagnosing and Managing Hyperinsulinemia in Horses  

• August 13, 2024
• ⎯ Nancy S. Loving, DVM

Hyperinsulinemia is an important risk factor for laminitis in horses. Identifying insulin-resistant horses is key to preventing and managing laminitis. At the 2024 Tex Cauthen Farrier/Veterinarian/Researcher Seminar in Kentucky, Andrew van Eps, BVSc, PhD, MACVSc, Dipl. ACVIM, professor at University of Pennsylvania’s New Bolton Center, proposed multiple ways to evaluate a horse’s insulin levels.  

A Closer Look at Adiponectin 

Adiponectin (high molecular weight) is a protective adipokine from fat cells that improves insulin sensitivity and serves as a metabolic health marker. It is insulin-sensitizing and acts as an AMP-activated protein kinase (AMPK) agonist to inhibit mTor (which promotes the activation of insulin receptors and insulin-like growth factor receptors). Adiponectin might also protect tissues against cell stress—both endoplasmic reticulum and oxidative—and ischemia. Low serum adiponectin is associated with insulin dysregulation. It is also an independent risk factor for laminitis when its levels are low. 

Adiponectin doesn’t fluctuate with stress or feeding. Researchers have compared adiponectin levels in horses fed carbohydrate or fat-based diets for more than 20 weeks. Horses in both categories developed high leptin levels, but only the carbohydrate-fed horses developed insulin dysfunction (ID) along with low serum adiponectin. 

Veterinarians can evaluate baseline fed insulin as a random test in nonfasted horses. This test involves taking blood samples two hours after feeding or pasture access. It is a reasonable predictor of insulin dysregulation. Insulin levels of 25 mIU/ml are 80% sensitive and 85% specific for ID. 

Van Eps reported that a combination of high insulin and low adiponectin is a red flag. Normal insulin and low adiponectin is a warning sign, but it is possible that a single sample doesn’t account for insulin fluctuations throughout the day. Doing a sugar or feed challenge might identify hyperinsulinemia with a potentially higher risk of laminitis. It is important to monitor and screen these horses. 

Attempts to increase adiponectin through diet and exercise are not particularly helpful, especially because certain breeds are inherently at risk for low adiponectin, including Welsh ponies and donkeys. Thoroughbreds have a lower risk. Sick and hospitalized horses with laminitis reportedly also have low adiponectin levels. 

Treating Hyperinsulinemia in Horses: Diet Is Key 

Dietary changes, along with exercise and pasture management, are critical to controlling hyperinsulinemia in horses. Besides feeding forage with < 10% nonstructural carbohydrates (NSC), van Eps emphasized the benefits of soaking hay to reduce sugars and starches in the feed. Study results indicate soaking grass hay in five gallons of room-temperature water for up to two hours provides the maximum benefit for controlling sugar and starch levels. Alfalfa is lower in NSCs than grass hay, but soaking alfalfa is also helpful.  

The European College of Equine Internal Medicine offers guidelines for dietary control of obesity in horses , which is a risk factor for insulin dysregulation. Rather than feeding horses 2% of their bodyweight in forage, owners can feed 1.4-1.7% of their bodyweight to achieve weight loss. These horses should not eat supplementary feeds. Exercise can also help horses lose weight.  

Medications to Control Insulin 

Van Eps described available medications for controlling insulin levels in horses: 

• Levothyroxine might assist with weight loss and insulin sensitivity. 
• There is no good evidence to support the long-term use of metformin, although it might have a targeted effect when administered at feeding time. It has poor oral bioavailability (4-7%) in horses. 
• Pergolide is useful for managing confirmed pituitary pars intermedia dysfunction. 
• Sodium glucose cotransporter-2 (SGLT-2) inhibitors help horses eliminate glucose through their urine. Currently, ertugliflozin (Steglatro) provides the most encouraging results, but it’s use is off label.   
• Semaglutide (Ozempic) is a glucagon peptide-1 (GLP-1) agonist mimetic shown to improve insulin responses to oral sugar testing in horses. Side effects include slow gastric emptying and slowed intestinal motility, which are risk factors for colic.  
• Pioglitazone (high molecular weight, HMW) might improve adiponectin increases over time. It blocks a specific cell receptor to facilitate growth of insulin-sensitive fat cells, which also produce adiponectin. This might be a slow way to control insulin, but van Eps noted it’s much less expensive than SGLT-2 inhibitors.  

Researchers evaluated pioglitazone’s effects on adiponectin concentrations and insulin responses after the oral sugar test. Fifteen horses and ponies received 2 mg/kg orally of HMW pioglitazone once daily for 28 days. The subjects were grouped into horses, ponies, and insulin dysregulated (ID) animals. Oral sugar tests were performed before and after treatment to measure adipokines on Days 0, 14, and 28. The researchers measured insulin and glucose levels on Days 14 and 28. The results indicated significant decreases in insulin responses to the oral sugar test at 90 and 120 minutes, especially after pioglitazone treatment in the ponies and insulin-dysregulated individuals. “Adiponectin concentrations were significantly increased in all groups after pioglitazone treatment,” the authors stated. They concluded that this medication provides positive effects for treating metabolic derangements in horses with ID and equine metabolic syndrome. 

Legere RM, Taylor DR, Davis JL, et al. Pharmacodynamic Effects of Pioglitazone on High Molecular Weight (HMW) Adiponectin Concentrations and Insulin Response After Oral Sugar in Equids. J Equine Vet Sci. Nov 2019; DOI: 10.1015/j.jevs.2019.102797

Related Reading

• Management of Hyperinsulinemia and Laminitis With Ertugliflozin
• The Effect of High Protein Diet on Insulin Dysregulated Horses
• The Effects of Adiposity and Weight on Equine Performance

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• Personal Finance

This Biden Administration Proposal Just Might Salvage Your Credit Report

Published on Aug. 13, 2024

By: Dana George

• The Biden administration wants medical bills removed from credit reports to prevent lenders from making decisions based on medical information.
• Medical bills do not serve as a fair indicator of how likely someone is to pay their bills.
• Medical debt is the top reason for bankruptcies in the U.S.

Medical debt is no joke. Not only does the U.S. have the most expensive healthcare of any country, but medical debt is the No. 1 reason Americans file for bankruptcy, according to the American Medical Association (AMA).

Medical debt can also play havoc on a person's budget and personal finances, causing their credit score to plummet and making it harder for them to get credit when it's needed.

A step in the right direction

Two years ago, Experian, Equifax, and TransUnion -- the big three national credit reporting agencies -- announced that they were removing medical debts under $500 from credit reports. Removing relatively small medical debts certainly represented a step in the right direction for many U.S. consumers. Still, 15 million Americans were left with $49 billion worth of medical bills on their credit reports.

The Biden administration plan would force credit reporting agencies to go much further.

What this could mean for you

What matters is how erasing medical debt from credit reports will impact the everyday American. If your credit report currently lists medical expenses among your unpaid debts, here's how this change may benefit you.

Your credit score will get a boost

The Consumer Financial Protection Bureau (CFPB) estimates that Americans with medical debt on their credit reports will see their credit scores rise by an average of 20 points once medical debt is removed. A sudden 20-point boost may be just what some households need to qualify for a loan or open a credit card.

Your medical issues won't be public knowledge

While credit reports only show that you have outstanding medical debt and don't spell out the specifics, anyone who sees a copy of your report can tell that you (or someone in your household) have been ill.

That may not matter if you're applying for a car loan, but it could impact whether a potential employer checking your credit report decides to take a chance on you or a landlord believes you can pay your rent every month. In other words, it raises questions that should never be raised.

Your medical equipment cannot be repossessed

The proposed rule would prevent lenders from repossessing medical equipment like wheelchairs if you can't repay a loan.

Bill collectors can no longer use medical debt as a weapon

According to the CFPB, under the current system, medical debt collectors use the credit reporting system to force people to pay debts -- some of which they may not owe. CFPB reports that many debt collectors use a practice known as "debt parking."

Here's how it works: Debt collectors purchase medical debt at a discount. They then place the debt on your credit report, typically without your knowledge. It's only when you apply for credit that you discover that medical debt is holding you back from loan approval.

If you really need that loan, you may feel forced to pay the medical bill just to get it off your credit report and improve your credit score. Once medical debt no longer shows up on credit reports, bill collectors can no longer use this manipulative tactic.

This doesn't mean debt will be erased

There are undoubtedly benefits associated with having medical debts removed from your credit report, but you will still be responsible for repaying the debt . The point of removing the debt from your report is to make it easier for you to carry on with your financial responsibilities and get back on your financial feet.

The CFPB will continue to accept comments and feedback on the Biden administration proposal through Aug. 12, 2024. If all goes well, the rule will be finalized early next year.

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Dana is a full-time personal finance writer, with more than two decades of experience. Her focus is on helping readers feel less alone as they navigate their personal finances and offering actionable insights.

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ORIGINAL RESEARCH article

E-learning pills on immunotherapy in urothelial carcinoma: the e-pimuc program for continuing medical education.

• 1 Catalan Institute of Oncology, Barcelona, Catalonia, Spain
• 2 Germans Trias i Pujol Health Science Research Institute (IGTP), Barcelona, Catalonia, Spain

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The high incidence and mortality rates of urothelial carcinoma mean it remains a significant global health concern. Its prevalence is notably pronounced in industrialized countries, with Spain registering one of the highest incidences in Europe. Treatment options are available for various stages of bladder cancer. Moreover, the management landscape for this disease has been significantly transformed by the rapid advances in immunotherapy. Healthcare professionals who diagnose, treat, and follow up with bladder cancer patients need comprehensive training to incorporate these advances into their clinical practice. To bridge these knowledge gaps, we set up the E-PIMUC program to educate healthcare professionals on bladder cancer management and specifically immunotherapy.Methods: E-PIMUC used an innovative microlearning methodology comprising bitesize learning pills that support efficient acquisition of specialized expertise. We used a mixed methods, quantitative and qualitative approach to assess the success of the E-PIMUC program. Data collection encompassed prepost testing, participation metrics, satisfaction surveys, and self-perceived performance assessments.Results: A total of 751 healthcare professionals enrolled in the program. Of these, 81.0% actively engaged with the content and 33.2% passed all tests and were awarded the course certificate and professional credits. The course received satisfaction ratings of 94.3% to 95.1% and significantly improved the declarative knowledge of participants who had a range of professional profiles (p<0.001). Participants reported increased confidence in applying immunotherapy principles in their practice (average improvement of 1.4 points). Open-ended responses also underscored participants' perceived benefits, including expanded knowledge and enhanced patient interaction skills.The E-PIMUC program provided effective, comprehensive, cutting-edge training on bladder cancer management, particularly on the use of immunotherapy in this area of oncology. The high participation rates, positive satisfaction scores, substantial knowledge enhancement, and improved self-perceived performance, are all testament to the program's success. E-PIMUC was endorsed by regulatory bodies as a trusted educational resource in urothelial carcinoma management. What is more, complementary initiatives brought together patients and medical experts to foster a holistic, patient-centered approach to the complexities of bladder cancer care.

Keywords: urothelial carcinoma, Bladder cancer, Immunotherapy, healthcare professionals, Continuing medical education, Microlearning, e-learning pills

Received: 02 Feb 2024; Accepted: 12 Aug 2024.

Copyright: © 2024 Romero-Clarà, Madrid, Pardo, Ruiz De Porras, Etxaniz, Moreno-Alonso and Font. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Olga Romero-Clarà, Catalan Institute of Oncology, Barcelona, 08908, Catalonia, Spain Juan Carlos Pardo, Catalan Institute of Oncology, Barcelona, 08908, Catalonia, Spain Olatz Etxaniz, Catalan Institute of Oncology, Barcelona, 08908, Catalonia, Spain Deborah Moreno-Alonso, Catalan Institute of Oncology, Barcelona, 08908, Catalonia, Spain Albert Font, Catalan Institute of Oncology, Barcelona, 08908, Catalonia, Spain

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