EVALUATION OF THE OTTAWA KNEE RULE IN A PEDIATRIC E. D. AND UNIVERSITY SPORTS MEDICINE CENTER
Bibliographic record
Abstract
The Ottawa Knee Rule (OKR) is a clinical decision rule to screen for the use of radiography to identify fractures in adults. It has reduced radiography and costs in emergency departments (E.D.) without missing clinically significant fractures (Stiell et al. 1997) but has not been evaluated in other settings. We prospectively evaluated the OKR for its ability to identify knee fractures and to reduce radiography in pediatric and adult patients in a university sports medicine center and a pediatric E. D. All patients with acute knee injury (≤ 7 days old) had the rule applied and underwent radiography. Exclusion criteria included: age < 5 y, multiple trauma, altered consciousness, metabolic bone disease, fever, limp without injury, pregnancy, or previous evaluation of same injury. 129 patients (mean age 17.7 ± 10.2y, range 5–73y, 70% ≤ age 17y) had radiography after application of the rule. There were 4 fractures (all pediatric) for a fracture prevalence of 3%. Sensitivity was 1.0 (95% confidence interval {0.79, 1.0}), specificity 0.42 {0.41, 0.44}, positive predictive value 0.05, negative predictive value 1.0. OKR successfully identified all patients with fractures. Low fracture prevalence limited positive predictive value, but radiography could have been reduced by 41% (53/129) with a subsequent cost saving of > $6000. We conclude that the OKR could significantly reduce knee radiography in sports medicine centers and pediatric E. D. s without missing fractures. Prospective implementation in these clinical settings will determine how cost effective these rules will be.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".