Evidence-based medicine and the search for a science of clinical care
Bibliographic record
Abstract
EVIDENCE-BASED MEDICINE AND THE SEARCH FOR A SCIENCE OF CLINICAL CARE Jeanne Daly Berkeley: University of California Press 2005, HB 276 pp, US 65.00 ISBN. 0 520 24316 1Jeanne Daly documents how evidence-based medicine turned from a subversive set of ideas of single-minded people across the globe, into the latest buzzword in clinical science. In contemporary health care, to be against evidence-based medicine is to be against science, progress, and rationality. Evidence-based medicine stands for practicing medicine with a scientific basis. Medical decision-making should be guided by the best available, carefully evaluated quantitative evidence rather than by the experiences of medical authorities. For supporters, evidence-based medicine is the answer to incompetent physicians, clinicians overwhelmed by an endless stream of research articles, lack of efficient interventions, practice variation, cost-overruns, lingering clinical uncertainties, stale medical education, and health care inequities. For critics, evidence-based medicine 'rips the heart out of medicine' to replace it with an algorithm. Evidence-based medicine, critics further argue, is insufficiently clinical, methodological myopic, and leads to mindless standardisation. Still, in one generation the critics have been put in a defensive position, forced to explain that with all the problems in health care why wouldn't better science improve results.Daly offers the history of evidence-based medicine and clinical epidemiology based on, what can be described methodologically as, intellectual network studies. The core of her book consists of in-depth interviews with leading evidence-based figures in Canada, United States, Britain and South Africa who comprise an international network of like-minded thinkers. She introduces a figure with a short characterisation (e.g. 'The Internationalist: Kerr White' 2005:40), and then sums in a couple of pages the person's career path and achievements based on their published record and interview data.So, how do you become a leading figure in evidence-based medicine? You are a male clinician trained in the sixties. You are deeply frustrated with what you consider wrong-footed medicine, and you question the authority of your teachers. You decide to discover the 'true science' behind medical interventions. You become a strong believer in the randomised clinical trial or in meta-analyses of clinical trials. You do some studies. Besides strong research credentials, you also have a knack for convincing your initially sceptical colleagues. Soon, you are spreading the evidence-based medicine gospel through education, databanks, textbooks, and institutes. You have become mainstream medicine. You ignore those who question your authority. All around the world, the career paths seem remarkably similar.Among the pioneers, the most effective advocate of evidence-based medicine is the charismatic David Sackett. At McMaster University in Canada, Oxford University in Britain, and in hundreds of talks, he links scientific evidence to the bedside. Sackett has a talent for inspiring colleagues to ignore established dogma, drawing in talents foreign to medicine, and building institutions. Sackett was inspired by Alvan Feinstein who had challenged medicine with a broad intellectual, research-based agenda to turn the clock back to a time of diagnostic taxonomies of clinical symptoms, relying on better medical technologies and quantitative principles. Another dominant figure was Archie Cochrane who made the case for randomised clinical trials as a way to eradicate bias in research studies. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.093 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.004 | 0.057 |
| Scholarly communication | 0.028 | 0.033 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.017 | 0.038 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".