The need for clinical judgement in the application of evidence-based medicine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Evidence-based medicine (EBM) has no doubt resulted in great improvements in the practice of medicine. However, there are problems with overly zealous application of EBM, that for some amounts to religious practice. When good evidence exists, it should guide therapeutic and diagnostic choices. However, when evidence is lacking for a given patient, medicine is best practised by extrapolation from available evidence, interpreted in the light of the pathophysiology of the condition under consideration, and effects of various therapies in relation to that pathophysiology. OBJECTIVE: To assess ways in which the unthinking application of EBM can go wrong; these include withholding therapy in patients whose subgroup was excluded from clinical trials, blind acceptance of the numbers, reliance on studies with crucial design flaws and reliance on intention-to-treat analysis when it is not appropriate. STUDY SELECTION: Examples assessed included withholding cholesterol-lowering therapy in the elderly, not using B-vitamin therapy for stroke prevention, not using revascularisation for true renovascular hypertension and avoiding statin therapy for fear of intracerebral haemorrhage. FINDINGS: Zealous application of EBM is often inappropriate. CONCLUSIONS: In some instances, when there is a lack of evidence, or faulty interpretation of the evidence, clinical judgement should inform the application of EBM.
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.553 | 0.436 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.018 | 0.005 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.010 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it