‘We hold these truths to be self‐evident’: deconstructing ‘evidence‐based’ medical practice
Bibliographic record
Abstract
Rationale, aims and objectives Evidence-based medicine (EBM) claims to be based on 'evidence', rather than 'intuition'. However, EBM's fundamental distinction between quantitative 'evidence' and qualitative 'intuition' is not self-evident. The meaning of 'evidence' is unclear and no studies of quality exist to demonstrate the superiority of EBM in health care settings. This paper argues that, despite itself, EBM holds out only the illusion of conclusive scientific rigour for clinical decision making, and that EBM ultimately is unable to fulfil its own structural criteria for 'evidence'. Methods Our deconstructive analysis of EBM draws on the work of the French philosopher, Jacques Derrida. Deconstruction works in the name of justice to lay bare, to expose what has been hidden from view. In plain language, we deconstruct EBM's paradigm of 'evidence', the randomized controlled trial (RCT), to demonstrate that there cannot be incontrovertible evidence for EBM as such. We argue that EBM therefore 'auto-deconstructs' its own paradigm, and that medical practitioners, policymakers and patients alike ought to be aware of this failure within EBM itself. Results EBM's strict distinction between admissible evidence (based on RCTs) and other supposedly inadmissible evidence is not itself based on evidence, but rather, on intuition. In other words, according to EBM's own logic, there can be no 'evidentiary' basis for its distinction between admissible and inadmissible evidence. Ultimately, to uphold this fundamental distinction, EBM must seek recourse in (bio)political ideology and an epistemology akin to faith.
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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 | 0.098 | 0.141 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.010 | 0.236 |
| Scholarly communication | 0.026 | 0.039 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".