Safety and efficiency of the Ottawa ankle rule in a Swiss population with ankle sprains
Bibliographic record
Abstract
OBJECTIVES: We examined the accuracy of the Ottawa Ankle Rule (OAR) to rule out ankle and mid-foot fractures in patients presenting with acute ankle sprain and differences of accuracy between surgeons and non-surgeons. DESIGN: Prospective cohort study. SETTING: Swiss urban secondary care centre. PARTICIPANTS: Between September 2001 and October 2002 359 patients presented with a case of ankle sprain. Of these, 251 patients both met recruitment criteria and provided data for this study. A group of surgeons and non-surgeons assessed the OAR and all patients underwent blinded radiographic assessment. MAIN OUTCOME MEASURES: Sensitivity, specificity of the OAR. RESULTS: Of the 251 patients with ankle sprains 33 had an ankle fracture (13%) and none had a mid-foot fracture. All cases with a fracture had a positive OAR result (sensitivity 100% 95% CI; 89-100) and of 218 patients without a fracture, the OAR was negative in 45 cases (specificity 21%; 16-27). In the subgroup of patients assessed by surgeons, sensitivity was 100% (77-100) and specificity was 32% (20-46). In the non-surgical group, sensitivity was also 100% (82-100) but specificity was lower (17% (11-23). CONCLUSIONS: This validation study of the OAR in a Swiss setting produced similar results than those published previously in various other settings. We found differences in the performance of the rule between surgical and non-surgical staff indicating that the OAR has its interpretation component which is more difficult to judge properly by well-instructed non-surgical assessors.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
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".