Bibliographic record
Abstract
Commentary on the paper by Riordan et al It has become axiomatic that high quality health care requires application of the best available evidence in the context of the individual patient’s situation. Medical schools and residency training programmes are required to provide training in critical appraisal of the literature, and no self respecting guideline would claim to be other than “evidence based”. In spite of this wide acceptance of evidence based medicine as the right thing to do, it is clear, from studies such as the one by Riordan et al in this issue, that we are just not quite there yet.1 These investigators wondered whether “best paediatric evidence” was accessible and used by on-call doctors working at inpatient paediatric and neonatal units. What they found was perhaps predictable: the sources they defined as “best paediatric evidence” were generally accessible, but they were not often used. Other studies suggest that the problem is widespread: only a minority of Canadian internists reported using evidence based information sources,2 and similar results were found on a survey of family practitioners in New Zealand.3 Fewer than 5% of Australian general practitioners had ever used the Cochrane Library in 1999.4 Insufficient time, inadequate skills, and limited access to evidence are the most commonly cited reasons that physicians give for not seeking and using evidence more consistently.5 The practice of evidence based medicine has been conceptualised as a five step process: recognising information needs and describing them in well formulated clinical questions; efficiently finding information; critically appraising the information; applying the …
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 | 0.034 | 0.207 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.013 | 0.004 |
| Research integrity | 0.054 | 0.075 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".