A Brief History of Evidence-Based Medicine (EBM) and the Contributions of Dr David Sackett
Bibliographic record
Abstract
In January 2007 the British Medical Journal contacted an online poll of its readers, and evidence-based medicine (EBM) was ranked seventh among the 15 most important milestones that shaped modern medicine. These 15 milestones included such things as the introduction of antibiotics, immunization, sanitation, and radiology.1 A Google search today on websites pertaining to anything evidence-based will be in the millions. EBM is defined as integration of the best research evidence with clinical expertise and patient values.2,3 An extension of EBM that is more relevant today is Evidence-Based Clinical Practice, which takes into account the healthcare setting and circumstances in which we practice.4 The EBM movement started in 1981 when a group of clinical epidemiologists at McMaster University (Hamilton, Ontario, Canada), led by David Sackett, published the first of a series of articles in the Canadian Medical Association Journal advising physicians how to appraise the medical literature.5 The actual term “evidence-based medicine” was first coined by Gordon Guyatt, the Program Director of Internal Medicine at McMaster University from 1990 to 1997, who was and one of Sackett's mentees in 1991.6
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".