Bibliographic record
Abstract
Purpose: The Ottawa ankle rule (OAR) is a clinical decision rule to detect bony injury in patients with a recent ankle injury. We evaluated the ability of emergency medical technicians (EMTs) to accurately apply and interpret the OAR. Methods: This prospective study was done from October 2009 to February 2010 in a secondary teaching hospital. Patients >18 years of age presenting at the emergency department within 48 hours of a single ankle injury were included. Seven EMTs and three emergency medicine residents were trained in the application of the OAR through one hour educational session prior to this study. They examined the patient`s ankle and recorded the data separately. Sensitivity and specificity of the OAR and interobserver agreement using the Kappa statistic were determined. Results: Fifty-one patients were enrolled, mean age was 40.9 years, and 33 (64.7%) were male. Substantial to good agreement were found for all criteria of the OAR (p<0.001). The sensitivity of the OAR was 100% with a specificity of 27.8% in both of them. Conclusion: To the best of our knowledge, our study is the first to assess the ability of EMTs working in an emergency center to evaluate and interpret the OAR in adult patients with acute ankle injury. Even less-experienced EMTs can accurately apply and interpret the OAR. The incorporation of the OAR into the EMT`s assessment of ankle-injury patients may be a useful physical examination tool for prehospital and hospital triage.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".