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I enjoy reading your briefs and especially this one. I was introduced to this issue by one of my mentors, Dr. William S. Yamamoto, Chair of the Department of Clinical Engineering at the GWU Medical School in 1975 when I was working for him. He introduced me to the concepts of cluster analysis and fuzzy logic as a potential means of resolving some of these issues or, at least illustrating them to clinicians when they were going through the diagnostic process. He then moved back a step and introduced me to the concepts published soon after by Galen and Gambino in their book "Beyond Normal: The predictive value and efficiency of medical diagnoses" around the same time regarding Positive and Negative Predictive Value and how prevalence of a disease in a population will interact to yield unexpected and unwanted results.
Later, I became a pathologist and personally witnessed this problem. How laboratory tests are set up regarding cutoffs between "normal" and "abnormal" for, say creatinine or "no evidence of a myocardial infarct" versus "evidence of a myocardial infarct is present" for CKMB and now Troponin will greatly affect who is diagnosed with a disease and who is not. This becomes a serious societal issue when we apply tests to screen large numbers of people who have no clinically evident disease even with tests stated to have 95% sensitivity and 95% specificity and I refer you to an excellent book titled "Over Diagnosis: Making People Sick in the Pursuit of Health" by Dr. H. Gilbert Welch. I have personally seen and reported on this issue and refer you to my DEM 2016 poster: Slide 1 (manxenterprises.com) and DEM 2017 poster: Slide 2 (manxenterprises.com)
Mark Gusack, M.D.
MANX Enterprises, Ltd.