Evidence‐based medicine among the dreaming spires of Oxford: the Pfizer Pharmacy Grant 2014
Bibliographic record
Abstract
To the Editor, In November 2014, I attended a short course at Oxford University, Teaching Evidence-Based Practice, with assistance from an SHPA grant.The course was run by the Centre for Evidence-Based Medicine (CEBM) and the University of Oxford Department for Continuing Education.The CEBM is a recognised world leader in evidence-based practice.This was an intensive course for those who already have skills in evidence-based medicine, and focused on the teaching of critical appraisal and evidence-based practice.The NSW Medicines Information Centre (MIC) runs courses for pharmacists in Medicines Information.Critical appraisal is an integral part of both the introductory and advanced courses offered by the MIC.I wanted to attend the course to improve my teaching skills in an area that many people find dry and overly technical.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.078 | 0.020 |
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".