A retrospective clinical audit of acute ankle sprains within the Gold Coast Hospital Health Service Emergency Departments
Bibliographic record
Abstract
Introduction: Ankle and foot related injuries are the most prevalent musculoskeletal injury within the Australian ED setting. The purpose of this study was to outline and benchmark the current management of acute ankle injury within the ED settings in the Gold Coast Hospital and Health Service. Methods: A retrospective cross-sectional design was used. The study was approved as a Quality Audit (LNR/2018/QGC/46335). A total of 400 charts with the diagnosis of ankle sprain/ strain were reviewed and relevant data was extracted and analysed. Results: A total of 241 (60.3%) were female and the median age of the entire cohort was 24.0 (IQR of 15 – 41). Mechanism of Injury was recorded in 99.3% of the charts; injury history in 24.8% of charts and application of Ottawa Ankle Rules in 89% of charts. A minimum standard clinical diagnostic assessment involved the combination of all these variables above and was recorded in 30% of the charts. A total 98% of charts reported an Xray. At least one form of treatment was recorded in 99% of charts with load education only being reported in 15.5% of charts. Variation across Physiotherapists, Nurse Practitioners and Doctors was greatest for load education (55.1%, 2.9% and 4.5% respectively) and referral for follow up (64%, 13.2% and 24.7% respectively). Discussion: Discrepancies in management across practitioners and when benchmarked against clinical guidelines is evident. Specifically, injury history and load education need to be included in ankle management as these factors may lead to mechanical and sensorimotor impairments and contribute to ongoing ankle pathology.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".