DIAGNOSTIC VALUE OF OTTAWA ANKLE RULES: SIMPLE GUIDELINES WITH HIGH SENSITIVITY
Bibliographic record
Abstract
Purpose Ankle and foot injuries are one of the most common acute sport injuries. The objective was to conduct a study to determine the accuracy of the Ottawa Ankle Rules (OARs) to rule out ankle and midfoot fractures in athletes' population presenting with blunt ankle and midfoot injuries. Methods Two physicians assessed the OARs in 1156 athletes and 1329 radiographs. Radiography was performed in each patient after clinical evaluation. Findings were recorded. An expert radiologist who was blinded to clinical exanimation interpreted the radiographs. Findings Of the 1329 radiographs from ankle and midfoot trauma, 93 ankle and 71 midfoot fractures were diagnosed. All cases with a fracture had a positive OARs result (sensitivity 100% 95% CI; 97–100) and of 1165 radiographs without a fracture, the OARs were negative in 439 cases (specificity 37%; 34–40).The corresponding potential savings in radiographs was 33% using the OAR. The interobserver agreement for our two independent assessors was substantial. (κ=0.79, SE=0.11). Conclusion This validation study of the OARs in an Iranian athletes' population produced similar results than those published previously in various other settings. We confirmed the previous studies that the OARs could be a reliable tool to exclude fractures and could aid sport medicine physicians make proper decisions on field and in clinic about athletes who suffered from ankle and midfoot trauma. Employing OARs would significantly decrease radiography requests and costs.
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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 | 0.019 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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 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".