Accuracy of Ottawa Ankle Rules for Predicting Fractures in Acute Ankle and Midfoot Injuries
Bibliographic record
Abstract
Introduction: The incidence of acute ankle and mid-foot injuries are one of the most common reasons for presenting to emergency department and orthopedics outpatient departments, but only a small percentage of patients approximately 14% have clinically significant fractures. However, these patients are almost always sent for radiography. The Ottawa ankle rules have been designed to reduce the number of unnecessary radiographs ordered for patients with acute ankle and midfoot injuries. Accuracy of “Ottawa ankle rules” for predicting fractures in patients with acute ankle and midfoot injuries and to assess the potential of these tests to reduce unnecessary x-rays. Materials and methods: Study was conducted in the emergency department and department of orthopedics and traumatology in Madhesh institute of health sciences from April 2023 to April 2024. In this period 100 patients were included in this study, 50 patients in ankle and 50 patients in midfoot group. Outcome measures of this study were sensitivity, specificity, positive predictive value and negative predictive value of the Ottawa ankle rules. Results: Sensitivity of the Ottawa ankle rules for predicting fractures was 100% for each two group (ankle and midfoot group), and 100% for combined ankle and midfoot group. Specificity of the Ottawa ankle rules for predicting fracture was 0.26(26%) for combined ankle and midfoot group, 0.24(24%) for ankle group and 29.27% for the midfoot group. The potential of Ottawa ankle rules to reduce unnecessary radiographs was calculated 23%. Conclusions: Ottawa ankle rules are very accurate and highly sensitive tools for detecting fractures in acute ankle and midfoot injuries. Implementation of these rules would lead to significant reduction of radiographs, costs of treatment, radiation exposure and waiting time of patients in hospital.
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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 | 0.004 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".