Diagnostic accuracy of the Ottawa Ankle and Midfoot Rules: a systematic review with meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To review the diagnostic accuracy of the Ottawa Ankle and Midfoot Rules and explore if clinical features and/or methodological quality of the study influence diagnostic accuracy estimates. DESIGN: Systematic review with meta-analysis. DATA SOURCES: MEDLINE, EMBASE, CINAHL, SPORTDiscus and Cochrane Library. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Primary diagnostic studies reporting the accuracy of the Rules in people with ankle and/or midfoot injury were retrieved. Diagnostic accuracy estimates, overall and for subgroups (patient's age, profession of the assessor and setting of application), were made. Sensitivity analyses included studies with a low risk of bias and studies where all patients received radiographs. RESULTS: 66 studies were included. Ankle and Midfoot Rules presented similar accuracies, which were homogeneous and high for sensitivity and negative likelihood ratios and poor and heterogeneous for specificity and positive likelihood ratios (mean, 95% CI pooled sensitivity of Ankle Rules: 99.4%, 97.9% to 99.8%; specificity: 35.3%, 28.8% to 42.3%). Sensitivity of the Ankle Rules was higher in adults than in children, but the profession of the assessor did not appear to influence accuracy. Specificity was higher for Midfoot than for Ankle Rules. There were not enough studies to allow comparison according to setting of application. Studies with a low risk of bias and where all patients received radiographs provided lower accuracy estimates. Specificity heterogeneity was not explained by assessor training, use of imaging in all patients and low risk of bias. CONCLUSIONS: Study features and the methodological quality influence estimates of the diagnostic accuracy of the Ottawa Ankle and Midfoot Rules.
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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 | 0.041 | 0.130 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.030 | 0.050 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".