Accuracy of ultrasound in diagnosing ankle injuries in emergency care
Bibliographic record
Abstract
BACKGROUND: Ankle injuries are one of the most common presentations in the ED. Although fractures can be ruled out using the Ottawa Ankle Rules, the specificity is low, which means many patients may still receive unnecessary radiographs. Even once fractures are ruled out, assessment of ankle stability is recommended to rule out ruptures, but the anterior drawer test has only moderate sensitivity and low specificity and should be performed only after swelling has receded. Ultrasound could be a reliable, cheap and radiation free alternative to diagnose fractures and ligamentous injuries. The purpose of this systematic review was to investigate the accuracy of ultrasound in diagnosing ankle injuries. METHODS: Medline, Embase and the Cochrane Library were searched up to 15 February 2022 to include studies of patients of 16 years or older presenting to the ED with acute ankle or foot injury, who underwent ultrasound and had diagnostic accuracy as outcome. No restrictions were applied for date and language. Risk of bias and quality of evidence using the Grading of Recommendations, Assessment, Development and Evaluations approach were assessed. RESULTS: Thirteen studies evaluating 1455 patients with bony injuries were included. In 10 studies, the reported sensitivity for fracture was >90%, but varied among studies between 76% (95% CI 63% to 86%) and 100% (95% CI 29% to 100%). In nine studies, the reported specificity was at least 91%, but varied between 85% (95% CI 74% to 92%) and 100% (95% CI 88% to 100%).Six studies including 337 patients examined the use of ultrasound for ligamentous injuries and found a sensitivity and specificity >94% and 100%. Overall quality of evidence for both bony and ligamentous injuries was low and very low. CONCLUSION: Ultrasound has the potential to be a reliable method for diagnosing foot and ankle injuries, however, higher grade evidence is needed. PROSPERO REGISTRATION NUMBER: CRD42020215258.
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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 | 0.039 | 0.284 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".