Can the Ottawa knee rule be applied to children? A systematic review and meta-analysis of observational studies
Bibliographic record
Abstract
BACKGROUND: The Ottawa knee rule (OKR), a clinical decision aid is used to reduce unnecessary radiography. It is not clear whether this rule can be applied to children. OBJECTIVE: To establish whether the OKR had adequate sensitivity and acceptable specificity in children to advocate widespread use. METHODS: A systematic review and meta-analysis was conducted of observational studies that examined the diagnostic characteristics of the OKR in children. DATA SOURCES: Relevant English language articles were identified from Medline (1950 to date), EMBASE (1974 to date), CINAHL (1982 to date), the Cochrane Library, Google Scholar and a hand search of bibliographies. STUDY SELECTION: Observational studies that included children and have used the OKR for ruling out fractures in children either radiologically or in combination with follow-up. RESULTS: Four relevant studies were identified. Three studies were suitable for inclusion in the meta-analysis, representing 1130 children. The pooled negative likelihood ratio was 0.07 (95% CI 0.02 to 0.29), the pooled positive likelihood ratio was 1.94 (95% CI 1.60 to 2.36), the pooled sensitivity was 99% (CI 94.4 to 99.8) and the pooled specificity was 46% (CI 43.0 to 49.1). The reduction in radiography was between 30% and 40%. CONCLUSION: The OKR has high sensitivity and adequate specificity for children over the age of 5 years. There are not enough good data to advocate application of the OKR in children less than 5 years.
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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 | 0.084 | 0.203 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.024 | 0.043 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".