Diagnostic accuracy of the Ottawa ankle rule to exclude fractures in acute ankle injuries in adults: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Ankle traumas are common presenting injuries to emergency departments in Australia and worldwide. The Ottawa Ankle Rules (OAR) are a clinical decision tool to exclude ankle fractures, thereby precluding the need for radiographic imaging in patients with acute ankle injury. Previous studies support the OAR as an accurate means of excluding ankle and midfoot fractures, but have included a paediatric population, report both the ankle and mid-foot, or are greater than 5 years old. This systematic review and meta-analysis aimed to update and assess the existing evidence of the diagnostic accuracy of the Ottawa Ankle Rule (OAR) acute ankle injuries in adults. METHODS: A systematic search and screen of was performed for relevant articles dated 1992 to 2020. Prospective and retrospective studies documenting OAR outcomes by physicians to assess ankle injuries were included. Critical appraisal of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. Outcomes related to psychometric data were pooled using random effects or fixed effects modelling to calculate diagnostic performance of the OAR. Between-study heterogeneity was assessed using the Higgins I2 test, with Spearman's correlation test for threshold effect. RESULTS: From 254 unique studies identified in the screening process, 15 were included, involving 8560 patients from 13 countries. Sensitivity, specificity, negative likelihood ratio, positive likelihood ratio and diagnostic odds ratio were 0.91 (95% CI, 0.89 to 0.92), 0.25 (95% CI, 0.24 to 0.26), 1.47 (95% CI, 1.11 to 1.93), 0.15 (95% CI, 0.72 to 0.29) and 10.95 (95% CI, 5.14 to 23.35) respectively, with high between-study heterogeneity observed (sensitivity: I2 = 94.3%, p < 0.01; specificity: I2 = 99.2%, p < 0.01). Most studies presented with low risk of bias and concern regarding applicability following assessment against QUADAS-2 criteria. CONCLUSIONS: Application of the OAR is highly sensitive and can correctly predict the likelihood of ankle fractures when present, however, lower specificity rates increase the likelihood of false positives. Overall, the use of the OAR tool is supported as a cost-effective method of reducing unnecessary radiographic referral, that should improve efficiency, lower medical costs and reduce waiting times.
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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 | 0.023 | 0.073 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.044 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".