Diagnostic Accuracy of Ottawa Knee Rule for Diagnosis of Fracture in Patients with Knee Trauma; a Systematic Review and Meta-analysis.
Bibliographic record
Abstract
Introduction: In order to improve the efficacy of requesting knee radiography and reduce unnecessary radiation exposure, some clinical decision rules have been proposed for the assessment of knee injuries. Among them, the Ottawa Knee Rule (OKR) was considered as one of the best guidelines with several validation studies. Therefore, in this meta-analysis, we aimed to investigate the accuracy of OKR for diagnosis of fracture in patients presenting with knee trauma. Methods: A systematic search was conducted in PubMed, Web of Science, Scopus, Google Scholar, and EBSCO from inception to September 2022. Quality assessment of the included studies was performed using QUADAS-2 tool. Diagnostic accuracy parameters were analyzed using random-effects model. Statistical analysis was performed using Meta-Disc and Stata softwares. Results: The meta-analysis of the 18 included studies (6702 patients) showed that the pooled sensitivity and specificity of OKR for diagnosis of fractures were 0.98 (95% CI: 0.96-0.99) and 0.43 (95% CI: 0.42-0.45), respectively. The pooled positive likelihood ratio (PLR) and negative likelihood ratio (NLR) were 1.56 (95% CI: 1.39-1.75) and 0.12 (95% CI: 0.05-0.26), respectively. The area under curve (AUC) of the hierarchical summary receiver operating characteristic (HSROC) curve was 0.54. Conclusion: This meta-analysis indicates that OKR has a high diagnostic performance for diagnosis of fracture, with a pooled sensitivity of 98% and a pooled specificity of 43%. These results propose potential effects of OKR on reduction of unnecessary radiography, time spent in emergency departments, and direct and indirect costs, which should be confirmed using high-quality studies in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.035 | 0.089 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.044 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.001 |
| 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".