Ottawa Knee Rule: Investigating Use and Application in a Tertiary Teaching Hospital
Bibliographic record
Abstract
Background Knee injuries are encountered commonly in the emergency departments (EDs) in Ireland. Validated clinical decision rules such as Ottawa knee rule (OKR) can be used in acute knee injury settings to reduce the number of unnecessary radiography. Clinical judgment can be used to distinguish between suspected fractures and non-fractures in many cases; however, radiography is still routinely requested. Objectives We evaluated the OKRs in a high-volume tertiary teaching hospital in Ireland to determine whether the rule could be safely used to decide whether patients with acute blunt knee trauma should undergo radiography. Methods This was an observational study conducted in the ED over a three-month period in a tertiary referral hospital. A total of 110 patients with acute knee injuries were examined using OKR. Inclusion criteria included patients with acute knee injuries due to blunt trauma or twisting injury and patients with lacerations or contusions. Open fractures and fractures due to penetrating injury were excluded from the study. Results Fractures were seen in 12 (13.2%) of the 110 patents who met the inclusion criteria. The OKR predicted all 12 fractures. Sensitivity was 100%, and specificity was 39%. Conclusions The OKR is highly sensitive for fracture in this setting and can be safely used to decide whether patients with acute blunt knee trauma should undergo radiography.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".