Accuracy of Ottawa ankle rules to exclude fractures of the ankle and mid-foot: systematic review
Bibliographic record
Abstract
We believe that publishing surgeon specific, crude mortality data, 2 as is planned in the United Kingdom, is not in the best interests of patients, and our study shows that surgeons cannot be compared fairly in this way. Cardiac surgeons already work in a stressful environment, and the perception that a "bad run" might jeopardise their career or result in suspension and investigation may lead to a tendency to turn down high risk cases. The easiest way to obtain low mortality is to do only straightforward operations-so called risk averse behaviour. This has already been identified as a potential problem after a survey of all cardiac surgeons in the United Kingdom in 2000, where 94% of responders agreed that high risk patients were being turned down for surgery. 1 Death rates in these patients often approach 100% if the patients are denied surgery and patients at heightened risk from surgery are, in general, those who have the most to gain from a successful operation. 21 Our recommendation of benchmarking only low risk patients seems scientifically justified and pragmatic and should help to prevent risk averse behaviour.
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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 | 0.063 | 0.373 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".