Evaluating the Impact of Audits and Re-audits on Adherence to the Ottawa Knee Rules in a High-Volume UK Trauma Centre
Bibliographic record
Abstract
Background The Ottawa Knee Rules (OKR) are a validated clinical decision-making tool designed to minimise unnecessary radiographs in knee trauma, thereby reducing radiation exposure, optimising resource utilisation, and streamlining patient management. This study audits and re-audits the clinical compliance with OKR in radiography referrals by the Orthopaedic and Emergency Department (ED) teams at Salford Royal NHS Foundation Trust. Methodology A two-cycle retrospective audit was conducted, examining knee X-ray request forms submitted between October 2023 and March 2024 (Cycle 1) and March 2024 and September 2024 (Cycle 2). Each request was evaluated against the OKR criteria and cross-referenced with corresponding clinical notes. Target compliance was 100%. Educational interventions were implemented after Cycle 1 to improve adherence. Results In Cycle 1, only 41% of referrals documented at least one OKR criterion. This improved significantly to 91% in Cycle 2. Notable improvements were observed in specific OKR indicators, including documentation of inability to bear weight (14% to 57%) and isolated patellar tenderness (13% to 72%). Conclusions Educational interventions substantially improved OKR compliance among ED and Orthopaedic staff. Sustained efforts, including regular training and audits, are essential to maintain adherence, reduce unnecessary imaging, and ensure high-quality patient care.
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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 | 0.250 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".