Causality, mathematical models and statistical association: dismantling evidence‐based medicine
Bibliographic record
Abstract
From humble beginnings, largely at the medical school at McMaster University, Canada, the evidence-based medicine (EBM) movement has enjoyed a spectacular rise in international acceptance over the last 25 years. Randomized controlled trials (RCTs) and systematic reviews based on them have pride of place (the gold standard) in EBM's hierarchy of evidence; models and theories are relegated to the bottom of the hierarchy. In the last decade, RCTs have been extensively criticized. I briefly rehearse those criticisms because they are an important backdrop to the criticism of EBM developed in this paper. In essence, the argument developed here is that RCTs use mathematics solely as a tool of analysis rather than as the language of the science and that this fundamentally affects the validity of causal claims. As EBM gives pride of place to RCTs and devalues theoretical models - a devaluation that would be incomprehensible to a physicist or biologist - the validity of EBM's causal claims and knowledge claims are weak and far from a 'gold standard'.
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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 | 0.271 | 0.501 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.004 | 0.087 |
| Scholarly communication | 0.016 | 0.033 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.013 | 0.036 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".