Clinical audit and the implementation of the Ottawa Ankle Rules
Bibliographic record
Abstract
Purpose To implement the research‐based Ottawa Ankle Rules in a district hospital and audit their impact on the number and appropriateness of X‐rays for ankle injuries in A&E. Design/methodology/approach The method used was retrospective data collection, followed by education and prospective data collection on the management of subsequent ankle injuries. The computer records of the first 150 people presenting to A&E with ankle/foot injuries in one month were reviewed to determine whether the patient underwent an X‐ray, and what the results were. Every doctor working in A&E was then educated using a hand‐out giving the Ottawa Ankle Rules. The management of 150 people presenting with ankle/foot injuries in the month after this intervention was assessed. Findings There was a reduction in the number of patients receiving X‐rays (83/150 or 55 per cent versus 128/150 or 85 per cent pre‐intervention; p<<0.001). There was also an increase in the proportion of X‐rays showing fractures (17/83 or 20 per cent versus 16/128 or 12.5 per cent; difference not statistically significant). Research limitations/implications Possible to stimulate good practice with audit. Practical implications Improvement in practice stimulated by a motivated trainee doctor with appropriate support. Factors contributing to success discussed. Originality/value Encouraging example of successful audit, of interest to those interested in using clinical audit to improve 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 | 0.044 | 0.222 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".