Ottawa versus Pittsburgh knee rules in screening acute knee injury
Bibliographic record
Abstract
Introduction: Acute knee injuries account for eight percent of all injuries. Ottawa and Pittsburgh knee rules (OKR and PKR) were developed to assess the need for radiographs in acute knee injury. The objective of the study is to analyse the applicability of OKR and PKR to rule out fractures in acute knee injury. Method: This prospective cross-sectional study included 120 patients presenting with acute knee injury. Patients were assessed based on Ottawa and Pittsburgh knee rules (OKR and PKR) and radiographs were evaluated for fractures. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of OKR and PKR were calculated along with possible reduction in radiographs. Association of sex and age with outcome of acute knee injury was also analysed. Result: Among the 120 patients, 74(61.67%) were males and 46(38.33%) were females. Sensitivity, specificity, PPV and NPV of OKR were 95% CI 0.94(0.83-0.99), 95% CI 0.40(0.28-0.52), 95% CI 0.53(0.42-0.63) and 95% CI 0.90(0.74-0.98) respectively with possible reduction in radiographs by 31(25.83%). Sensitivity, specificity, PPV and NPV of PKR were 95% CI 0.88(0.76-0.05), 95% CI 0.57 (0.45-0.69), 95% CI 0.59(0.47-0.71) and 95% CI 0.87(0.75-0.95 ) respectively with possible reduction in radiographs by 46(38.33%). Conclusion: OKR and PKR are highly sensitive in ruling out fractures in patients with acute knee injury and more than one-fourth of radiographs can be avoided if these rules are applied.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 0.001 |
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".