Health Life

Why we should trust registered clinical trials

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In a time when we have to rely on clinical trials for COVID-19 drugs and vaccines, a new study in the Proceedings of the National Academy of Sciences (PNAS) brings good news about the credibility of registered clinical trials.

The authors are two Bocconi Professors of Economics, Jerome Adda and Marco Ottaviani, and a former MSc student of theirs, Christian Decker, now a Ph.D. candidate at the University of Zurich.

In a clinical trial, statistical significance is a key prerequisite for marketing approval of new drugs. Under a certain threshold of significance, the results of the trial could, in fact, be due to chance and not to the efficacy of the drug. Given the research costs involved and the lure of large potential profits by the sponsoring the trial, investigators may be pressured to beautify data, a standard argument goes. Previous studies of results of statistical tests reported in across a number of disciplines did actually detect an anomalous concentration of significance values immediately above the significance threshold, raising suspicions of selective reporting as well as manipulation.

Adda, Ottaviani and Decker concentrated their attention on the registered in ClinicalTrials.gov, the largest registry in the world, and focused on the differences in the results of phase II and phase III trials of the same drug. In phase II a drug’s efficacy is initially established in a small sample of people (usually in the low hundreds); in phase III safety and efficacy are then confirmed in a larger group of volunteers (usually in the low thousands). Reasonable expectations are that the of phase II will be confirmed in phase III and that trials that record a significance level under the threshold in phase II will be suspended.

Observing all

Health Life

Chatbots could be used to deliver psychotherapy during Covid-19 and beyond

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Chatbots could play a key role in helping people with issues around their health and wellbeing, according to a new study from academics at the University of Sheffield.

The study, led by Dr. Matthew Bennion from the University’s Department of Computer Science, argues that chatbots are an under-used resource and could be used to help more people talk through their issues.

While physical distancing measures are still in place and many health and have paused face-to-face meetings and are operating with reduced numbers of staff, the new research highlights how effective chatbots can be in helping to solve problems or deliver therapy now and also once the physical distancing measures are lifted.

Published in the journal JMIR, the study also reveals that many chatbots are primarily being used to serve .

However, results from the study show that people over the age of 65 have found chatbots useful and are willing to use them again to help address a problem.

The research compared the system usability, acceptability and effectiveness of two chatbots. One being a web-based adaptation of ELIZA, a chatbot originally developed in 1966, and the other MYLO—a developed by Dr. Warren Mansell at the University of Manchester.

When using MYLO, a user describes a current problem and the software then scans their text responses to ask curious questions designed to help them explore the problem in more depth and detail to reach their own solution.

MYLO uses a psychotherapeutic approach known as Method of Levels. The approach aims to keep patients focusing on their problem for a long enough period of time that they develop awareness of their internal conflict and generate new perspectives with the hope they’ll make the necessary changes they need to resolve the conflict.

When

Health Life

Artificial intelligence can improve how chest images are used in care of COVID-19 patients

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According to a recent report by Johns Hopkins Medicine researchers, artificial intelligence (AI) should be used to expand the role of chest X-ray imaging—using computed tomography, or CT—in diagnosing and assessing coronavirus infection so that it can be more than just a means of screening for signs of COVID-19 in a patient’s lungs.

Within the study, published in the May 6 issue of Radiology: Artificial Intelligence, the researchers say that “AI’s power to generate models from large volumes of information—fusing molecular, clinical, epidemiological and imaging data—may accelerate solutions to detect, contain and treat COVID-19.”

Although CT chest imaging is not currently a routine method for diagnosing COVID-19 in patients, it has been helpful in excluding other possible causes for COVID-like symptoms, confirming a diagnosis made by another means or providing critical data for monitoring a patient’s progress in severe cases of the disease. The Johns Hopkins Medicine researchers believe this isn’t enough, making the case that there is “an untapped potential” for AI-enhanced imaging to improve. They suggest the technology can be used for:

  • Risk stratification, the process of categorizing patients for the type of care they receive based on the predicted course of their COVID-19 infection.
  • Treatment monitoring to define the effectiveness of agents used to combat the disease.
  • Modeling how COVID-19 behaves, so that novel, customized therapies can be developed, tested and deployed.

For example, the researchers propose that “AI may help identify the immunological markers most associated with poor clinical course, which may yield new targets” for drugs that will direct the against the SARS-CoV-2 virus that causes COVID-19.


Radiologists use deep learning to find signs of COVID-19 in chest X-rays


More information:
Shinjini Kundu et al, How Might AI and Chest Imaging Help Unravel COVID-19’s Mysteries?, Radiology: Artificial Intelligence
Health Life

Better patient identification could help fight the coronavirus

Matching patient records from disparate sources is not only achievable, but fundamental to stem the tide of the current pandemic and allow for fast action for future highly contagious viruses. Credit: Regenstrief Institute

In a peer-reviewed commentary published in npj Digital Medicine, experts from Regenstrief Institute, Mayo Clinic and The Pew Charitable Trusts write that matching patient records from disparate sources is not only achievable, but fundamental to stem the tide of the current pandemic and allow for fast action for future highly contagious viruses.

Specifically, rapid identification of COVID-19 infected and at-risk individuals and the success of future large-scale vaccination efforts in the United States will depend, in part, on how effectively an individual’s electronic health data is securely shared among , care settings including hospitals and pharmacies, and other systems used to track the illness and immunization.

For data sharing to be effective, (EHRs)—both those held within a single facility and those in different healthcare organizations—must correctly refer to a specific individual. Is Billy Jones known at a different doctor’s office as William Jones and are all his health records linked? To which Maria Garcia do lab test results belong? Which John Smith was referred to during contact tracing?

Unfortunately, the commentary notes, patient matching rates vary widely, with healthcare facilities failing to link records for the same patient as often as half the time. Authors Shaun Grannis, M.D., for data and analytics at Regenstrief Institute and Regenstrief Professor of Medical Informatics at Indiana University School of Medicine; John D. Halamka, M.D., president of Mayo Clinic Platform and Ben Moscovitch, director of The Pew Charitable Trusts’ health information technology initiative, call for stakeholders to urgently address the patient matching conundrum. Otherwise, the commentary says, efforts to curtail the current pandemic