The Ottawa ankle rules for the use of diagnostic X-ray in after hours medical centres in New Zealand.
Bibliographic record
Abstract
AIMS: The aims of this study were to measure baseline use of Ottawa ankle rules (OAR), validate the OAR and, if appropriate, explore the impact of implementing the Rules on X-ray rates in a primary care, after hours medical centre setting. METHODS: General practitioners (GPs) were surveyed to find their awareness of ankle injury guidelines. Data concerning diagnosis and X-ray utilisation were collected prospectively for patients presenting with ankle injuries to two after hours medical centres. The OAR were applied retrospectively, and the sensitivity and specificity of the OAR were compared with GPs clinical judgement in ordering X-rays. The outcome measures were X-ray utilisation and diagnosis of fracture. RESULTS: Awareness of the OAR was low. The sensitivity of the OAR for diagnosis of fractures was 100% (95% CI: 75.3 - 100) and the specificity was 47% (95% CI: 40.5 - 54.5). The sensitivity of GPs clinical judgement was 100% (95% CI: 75.3 - 100) and the specificity was 37% (95% CI: 30.2 - 44.2). Implementing the OAR would reduce X-ray utilisation by 16% (95% CI: approx 10.8 - 21.3). CONCLUSIONS: The OAR are valid in a New Zealand primary care setting. Further implementation of the rules would result in some reduction of X-rays ordered for ankle injuries, but less than the reduction found in previous studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".