Aging, Author Interviews, JAMA, Opiods / 27.11.2019
Association of Disability With Mortality From Opioid Overdose
MedicalResearch.com Interview with:
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Dr. Yong-Fang Kuo[/caption]
Yong-Fang Kuo, PhD
Professor and Director, Office of Biostatistics
Don W. and Frances Powell Professor in Aging Research
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Dr. Mukaila Raji[/caption]
Mukaila Raji, MD, MS, FACP
Professor & Director
Edgar Gnitzinger Distinguished Professorship in Aging
Preventive Medicine and Population Health
UTMB Health
MedicalResearch.com: What is the background for this study?
Response: Medicare beneficiaries who qualified because of disability constitute a growing population of patients hospitalized for opioid/heroin overdose. Although the CDC regularly generates reports of opioid overdose deaths by demographics and states, studies on policy actionable predictors of overdose mortality (e.g., clusters of medical and psychiatric conditions, types of disabling conditions) are lacking in this population.
Dr. Yong-Fang Kuo[/caption]
Yong-Fang Kuo, PhD
Professor and Director, Office of Biostatistics
Don W. and Frances Powell Professor in Aging Research
[caption id="attachment_52317" align="alignleft" width="124"]
Dr. Mukaila Raji[/caption]
Mukaila Raji, MD, MS, FACP
Professor & Director
Edgar Gnitzinger Distinguished Professorship in Aging
Preventive Medicine and Population Health
UTMB Health
MedicalResearch.com: What is the background for this study?
Response: Medicare beneficiaries who qualified because of disability constitute a growing population of patients hospitalized for opioid/heroin overdose. Although the CDC regularly generates reports of opioid overdose deaths by demographics and states, studies on policy actionable predictors of overdose mortality (e.g., clusters of medical and psychiatric conditions, types of disabling conditions) are lacking in this population.
Dr. Villanti[/caption]
Andrea Villanti, PhD, MPH
Associate Professor
Department of Psychiatry
Vermont Center on Behavior and Health
University of Vermont
MedicalResearch.com: What is the background for this study?
Response: Our earlier work documented a significant association between first use of a flavored tobacco product and current tobacco use (
Dr. Helen Marsden PhD
Skin Analytics Limited
London, United Kingdom
MedicalResearch.com: What is the background for this study?
Response: In this technology age, with the explosion of interest and applications using Artificial Intelligence, it is easy to accept the output of a technology-based test - such as a smartphone app designed to identify skin cancer - without thinking too much about it. In reality, technology is only as good as the way it has been developed, tested and validated. In particular, AI algorithms are prone to a lack of “generalisation” - i.e. their performance drops when presented with data it has not seen before. In the medical field, and particularly in areas where AI is being developed to direct a patient’s diagnosis or care, this is particularly problematic. Inappropriate diagnosis or advice to patients can lead to false reassurance, heightened concern and pressure on NHS services, or worse. It is concerning, therefore, that there are a large number of smartphone apps available that provide an assessment of skin lesions, including some that provide an estimate of the probability of malignancy, that have not been assessed for diagnostic accuracy.
Skin Analytics has developed an AI-based algorithm, named: Deep Ensemble for Recognition of Malignancy (DERM), for use as a decision support tool for healthcare providers. DERM determines the likelihood of skin cancer from dermoscopic images of skin lesions. It was developed using deep learning techniques that identify and assess features of these lesions which are associated with melanoma, using over 7,000 archived dermoscopic images. Using these images, it was shown to identify melanoma with similar accuracy to specialist physicians. However, to prove the algorithm could be used in a real life clinical setting, Skin Analytics set out to conduct a clinical validation study.