Diagnostic Accuracy of the Ottawa Knee Rule to Rule out Knee Fractures Using MRI as Gold Standard
Bibliographic record
Abstract
ABSTRACT OBJECTIVES This study aimed to determine the diagnostic accuracy of the Ottawa Knee Rule for detecting knee fractures, using magnetic resonance imaging (MRI) as the reference standard. METHODOLOGY This prospective diagnostic accuracy study was conducted at the Department of Emergency Medicine, Lady Reading Hospital, Peshawar, from August 2023 to February 2024. A total of 96 patients with acute knee trauma were included using consecutive non-probability sampling. The Ottawa Knee Rule was applied clinically, followed by MRI evaluation. Diagnostic parameters, including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), likelihood ratios, and 95% confidence intervals (CI), were calculated. RESULTSThe Ottawa Knee Rule demonstrated a sensitivity of 86.1% (95% CI: 71.3-94.2) and specificity of 65.0% (95% CI: 51.5-76.6). The PPV was 59.6% (95% CI: 45.3-72.4), while the NPV was 88.6% (95% CI: 75.4-95.4). The overall diagnostic accuracy was 72.9%. The positive likelihood ratio (LR+) was 2.46, and the negative likelihood ratio (LR−) was 0.21. CONCLUSION The Ottawa Knee Rule demonstrated good sensitivity and moderate specificity; however, its performance was lower than that reported in meta-analyses. It remains a useful rule-out tool; however, findings should be interpreted cautiously due to limitations in MRI-based validation and study design.
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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 | 0.041 | 0.145 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".