Diagnostic Accuracy of Ottawa Ankle Rules in Acute Ankle Injuries in Patients Above Five Years of Age
Bibliographic record
Abstract
Ankle injuries are a common reason for emergency visits, but only 15% have fractures. The Ottawa Ankle Rules were introduced to reduce unnecessary imaging. Objective: To assess the diagnostic accuracy of Ottawa ankle rules in predicting ankle fractures and identify the main clinical predictors. Methods: This analytical cross-sectional study was conducted in the emergency department of Ghurki Trust and Teaching Hospital, Lahore, from July 2024 and March 2025 on consecutive patients with acute ankle trauma. OAR was used to evaluate patients, followed by radiography. Calculations were done on sensitivity, specificity, PPV, and NPV. Data were analyzed using frequencies and percentages for categorical variables and means with standard deviation for continuous variables. Results: In this cohort of 71 patients (66.2% male; mean age 36.6 ± 15.3 years), falls and road traffic accidents were the primary injury mechanisms. X-rays revealed fractures in 69.0% of the cases. The Ottawa Ankle Rules (OAR) achieved a sensitivity and negative predictive value of 100%, although the specificity was low at 13.6%, leading to 19 false-positive results. Notably, medial malleolus pain (p<0.001) and inability to bear weight (p=0.003) were the strongest predictors of fracture. Conclusion: Our study demonstrated 100% sensitivity and negative predictive value for detecting fractures and no false negatives, but specificity was low at 13.6%, resulting in 19 false positives. Fractures were present in 69.0% of cases and were found mostly to be bimalleolar (25.4%) and tri-malleolar (18.3%). Medial malleolus pain and inability to bear weight had the strongest capability to predict fractures clinically.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".