Moving Mountains: A Socratic Challenge to the Theory and Practice of Population Medicine. Michel Accad, M.D. College Station, Texas: Green Publishing House, LLC, 2017, 127 pp.
Many decades ago, as I sat in a class on the history of the English language, I found myself offended that suddenly linguistics, the “scientific” study of language, had been introduced into the history of English. The instructor was much more interested in Noam Chomsky’s new theories of transformational grammar than he was in the Danish contribution to the Old English language.
The drooling after all-things-science was just finding it’s way into the study of humanities back then. By the time I hit graduate school again, even the study of writing had been taken over by the numbers game. Great efforts were being made to get at what psychologists call the mind’s “black box” as one composed the written word.
Science envy had taken over much of the world of thinking. Nonetheless, I found the work of John Hayes and Linda Flower very interesting, especially in terms of the differences between the actions of expert writers as opposed to novice writers. The differences, by the way, bear a strong resemblance to the Dreyfuss brothers’ findings on what makes an expert an expert. It isn’t just more data or “bigger” data. Expertise resides in the ability to recall a single relevant occurrence some 30 years ago.
At this same time I also became aware of how pressure could be applied to research subjects to come to an agreement about something which was pretty much opinion. As graduate students, we taught freshman comp, and we participated in “scientific” studies of student writing. There was little chance a room full of graduate students were all going to evaluate various samples of student writing to be equally good or bad. But we soon learned that until everyone came up with enough agreement to generate something conducive to some kind of a statistical analysis, we weren’t going to be let out of the room.
“Evidence-based” is, well, let’s be honest here, really open to a lot of finagling. Mathematics has never been my strong suit, and statistics even less so. But my instincts tell me a lot of medical research is not nearly as cut and dried as it’s cracked up to be. Throw out the subjects that have more problems than what’s being tested for so the subjects are “more alike.” I may not be able to construct a Chi-square, but I also know that the “outliers” in research, the subjects removed from the pool for various reasons, do not make the pool of remaining subjects somehow a greater proof of the targeted conclusion.
…individual destinies invariably defy prediction.
Dr. Michel Accad, in his book Moving Mountains, has taken on the task of explaining how statistics are manipulated in medical studies. And he does so without pages of complex mathematical equations or tossing statistical jargon into his demonstration. The ubiquitous bell curve is the only statistical “tool” in the book. He has managed to articulate much of what I, and I suspect many others, sense about all these medical research studies, but lack the statistical facility to articulate.
Dr. Accad uses the work of Geoffrey Rose and his bell curves of population risk to develop the notion that there is a difference between treating a population and treating an individual. Setting the stage to portray Socrates, Dr. Accad asks Geoffrey Rose questions in the classic paripatetic teaching mode. Without resorting to a bevy of statistical numbers, the problems with the notion of population risk as opposed to individual risk can be seen even by those who have difficulty with classic statistics.
Except in the case of infectious disease, direct causal explanations are difficult to establish for most illnesses. Consequently, twentieth century medical science became almost exclusively dependent on the demonstration of statistical relationships to make causal inferences.
p.94, n 88
Confining medical research to studies of population risk denigrates the expertise physicians have gained by years of observing patients as individuals.
When physician judgment must “reflect the best available evidence in the biomedical literature,” then it is no longer a judgment but an application of rules / and algorithms that favor population outcomes over individual destinies.
The practice of medicine requires physicians to look at the various representations of risk and “evidence-based” findings and figure out if any of those notions have any relevance to the patient. Only then is the physician using his or her expertise in practicing medicine and only then is the patient getting the individualized care he or she deserves.
Even more astonishing, Dr. Accad accomplishes this overview of the shift in biomedical research by using story—the universal language of human understanding.