Diagnostic accuracy of Ottawa rules in diagnosing ankle fractures among patients taking X ray as gold standard.
Bibliographic record
Abstract
Objective: To determine the diagnostic accuracy of the Ottawa Ankle Rules (OAR) in diagnosing ankle fractures among patients, with X-ray imaging as the gold standard. Study Design: Cross-sectional study. Setting: Emergency Department of Lady Reading Hospital, Peshawar. Period: 1st January 2024 to 30th June 2024. Methods: 286 cases patients aged 18 to 60 years, presenting with ankle twisting and pain within 6 hours, were included. Exclusion criteria included patients unable to answer the Ottawa questionnaire or those refusing X-ray imaging. Following informed consent, patients were assessed using the Ottawa Ankle Rules and underwent X-ray imaging. The results were classified into true positives, false positives, true negatives, and false negatives. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated. Results: The mean age of participants was 36.73±6.7 years, with 67.13% males and 32.86% females. Ankle fractures were more common on the right side (62.93%). Among normal radiographs, 55.24% were correctly classified, while 18.18% were false positives. In patients with radiographic fractures, 23.77% were correctly identified. The sensitivity of the Ottawa Rules was 95.18%, while specificity was 56.67%. The positive predictive value was 68.72%, and the negative predictive value was 92.16%. Sensitivity was high in both males (92.73%) and females (94.55%), though specificity was lower in males (42.86%) compared to females (56.10%). Conclusion: The Ottawa Ankle Rules demonstrated high sensitivity for detecting ankle fractures but lower specificity.
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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 | 0.005 | 0.030 |
| 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 | 0.001 |
| 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".