Evidence‐based medicine. The good the bad and the ugly. A clinician's perspective
Bibliographic record
Abstract
Evidence-based medicine (EBM) has dramatically changed clinical practice but has had its share of both supporters and detractors. From a clinical perspective, EBM has improved critical thinking, empowered young clinicians/the public and allows for the generalizing research findings to large populations. However, one of the central problems with EBM is when it is used as a cookbook approach and independent thought is sacrificed in the process. Three examples of this include the inappropriate interpretation of significance testing, understanding the role of chance and examining subgroup effects. Perhaps the most scathing criticism of EBM is the susceptibility of clinical research to abuse and fraudulent practices. Therefore, as valuable as it has been to clinical practice, the practice EBM requires that it should be critiqued itself.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.038 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.014 | 0.028 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".