Diagnostic Accuracy of the Sonographic Ottawa Foot and Ankle Rules for Ankle and Foot Fractures in Emergency Department
Bibliographic record
Abstract
Aim: This study was carried out to determine whether the addition of a bedside ultrasound (US) to the Ottawa Foot and Ankle Rules (OAR) could decrease the need for radiographic imaging in the patients presenting to emergency department (ED) with foot and/or ankle trauma. Materials and Methods:In this prospective observational study, adult patients with acute foot and/or ankle injuries were included.Patients were first examined and OAR results were recorded.Then, US exam was performed by emergency physicians who were blinded to the OAR results.After that, the patients received radiography regardless of OAR exam and US findings.The US and OAR results were then compared to the formal radiography interpretation.Results: A total of 240 patients with a mean age of 36±12 years were included in the study of which 86 (35.8%) were female.The sensitivity of OAR in detecting foot and/or ankle fractures was 97.5% [95% confidence interval (CI): 86.8 to 99.9%] and the specificity of OAR increased from 48.5 % (95% CI: 41.4 to 55.7%) to 99.5% (95% CI: 97.2 to 100) with the addition of US.The OAR can reduce radiography by 40%, and if ultrasonography was used before radiography, there would be an approximately 72% reduction in X-ray requests.Conclusion: When used in conjunction with OAR, US can be used by trained physicians in ED to more accurately identify patients who would benefit from having an X-ray performed.US examination can further reduce the ordering of X-rays when compared to using OAR alone.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".