Validity of Ottawa Knee Rules at a Teaching Hospital of Eastern Nepal
Bibliographic record
Abstract
Introduction: Knee injuries are encountered frequently in Orthopedic emergency and Outpatient departments. Radiographs are routinely ordered in them, but not all of them demonstrate clear fractures. The decision for radiography based on subjective evaluation can help to reduce cost, decrease waiting time, and unnecessary radiation exposure. We lack this information in our context. Objective: The objective of this study was to find the validity of the Ottawa knee rule (OKR) in patients presenting with acute knee injuries at a teaching hospital in eastern Nepal. Methodology: A cross-sectional study was conducted from March 2018 to February 2019 including 210 cases of acute knee injuries. The patients were evaluated as per OKR and their X-rays were evaluated too. Collected data were entered in MS Excel and analyzed by SPSS for validity. Results: Out of the total of 210 eligible patients (122 males and 88 females) with a mean age of 43.97 years, the radiography rate was 100% but the yield rate was only 10.5%. Overall 69% of patients presented to the hospital within 24 hours of the injury and direct hit/trauma was the commonest mode of injury. Patella fractures were commonest followed by proximal tibia fractures. There was a high sensitivity of 100% and a specificity of 42.02%. The rule yielded a Positive and Negative Predictive value of 16.79% and 100%, respectively. The OKR, if applied correctly, could result in radiography rate reduction by 37.61%. The Fisher exact test result was significant at p<0.05. Conclusion: OKRs is a valid tool to predict fractures in patients who has a history of acute knee injuries without chances of missing fractures. This rule can reduce unnecessary radiography in our setup as well.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".