Applicability of Ottawa knee rule for knee injury in children
Bibliographic record
Abstract
OBJECTIVE: Previous studies have shown that the application of the Ottawa knee rule (OKR) reduces the need for radiographs in adults with acute knee injuries. Our objectives were to describe the epidemiology and incidence of knee injuries in children with acute knee trauma and to validate the OKR in a pediatric population. DESIGN: A prospective, consecutive study. SETTINGS: Two urban pediatric emergency departments. METHODS: All children 18 years of age and under who presented with acute traumatic knee injury of less than 1 week's duration, excluding patients with a normal knee examination, superficial skin injuries, prior history of knee injury, underlying bone disease, serious injuries involving two or more organ systems, or altered mental status were enrolled. Physicians assessed each patient for 22 standardized clinical findings prior to radiography. The OKR was applied to each patient by the investigating physician. RESULTS: All 234 patients eligible for the study had radiographs of the affected knee. The median age was 13 years with a range of 2 to 18 years. Using the OKR criteria for obtaining knee radiographs, 12 of 13 patients with fractures were identified (sensitivity 92%; 95% CI= 64-99). The missed case was an 8-year-old male who had sustained a nondisplaced fracture of the proximal tibia after a fall. If the OKR were applied to the pediatric population, it would have reduced the need for radiography in 46% of children. CONCLUSIONS: In the pediatric population studied, the OKR did not identify all patients with knee fractures. Future studies may consider modifying the OKR to accommodate the differences between pediatric and adult patients to improve the sensitivity of the rule while maintaining its specificity, before it can be applied routinely in clinical practice.
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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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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 | 0.000 |
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".