Validation of the Ottawa Knee Rule in Iran: a prospective study
Bibliographic record
Abstract
BACKGROUND: This study was designed to determine the accuracy of the Ottawa Knee Rule (OKR) when applied to patients with acute knee injury in the Iranian population of the Imam Hospital Emergency Department (ED) at. METHODS: This prospective cohort validation study included a convenience sample of all patients with a blunt knee injury sustained in the preceding 7 days presenting to the ED of a tertiary care teaching hospital during the study period. Patients were assessed for the five variables comprising the OKR, and a standardised data form was completed for each patient. Standard knee radiographs were ordered on all patients irrespective of the determination of the rule. The rules were interpreted by the primary investigator on the basis of the data sheet and the final orthopaedist radiograph reading. Outcome measures of this study were: sensitivity, specificity, positive predictive value and negative predictive value of the OKR. RESULTS: A total of 283 patients were enrolled in the study. 22 fractures (7.77%) were detected. The decision rule had a sensitivity of 0.95 (95% CI 0.77 to 0.99), and a specificity of 0.44 (95% CI 0.37 to 0.50). The potential reduction in use of radiography was estimated to be 41%. The OKR missed only one fracture. CONCLUSION: Prospective validation has shown that the OKR is a highly sensitive tool for detecting knee fractures and has the potential to reduce the number of radiographs in patients with acute knee injuries.
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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 | 0.015 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".