That’s why we clinicians should be waiting for our evidence-based expert sources to review the data and make recommendations before jumping on the bandwagon. Too many clinicians either embrace the news headlines about the latest discovery, or get persuaded to start unproven treatment by their patients who saw those same headlines.
Not that we should just do cookbook medicine, of course. But when we are weighing the pros and cons of various treatments for an individual unique patient, reviewing the latest reliable info is key. AND it’s easier to do now more than ever. When I started training back in dickety-nine or so, getting the latest info meant dashing off to the medical library’s card catalogue. Now, a few clicks and we’re there.
I relied heavily on UpToDate.com for such general medicine info while in practice, and helped make sure all our practitioners had ready access to it. It cost $750 a year per practitioner to fully access that site but was well worth it.
Well, in theory that is what pre-publication peer review and/or independent critical review for clinical application is supposed to do. Unfortunately, a lot of studies are distributed and reviewed as pre-prints, and it is rare for people to go back and verify that a peer reviewed release is still consistent. And, as noted, peer review often doesn’t really involve a detailed review of the statistical analyses because that is a lot of work (and often as not data is not provided or made generally available for papers in ‘closed’ journals). As @Qadgop_the_Mercotan notes, clinicians should be using “evidence-based expert sources to review the data and make recommendations”; unfortunately, the pool of people both skilled with statistical methods and sufficiently conversant with clinical application is not large nor growing. I suspect ‘the answer’ is going to be to start using “deep learning” AI systems for this application despite the essentially impossibility of actually validating these things to be reliable.