Effect of triage nurse-led application of the ottawa ankle rules on pain and patient satisfaction with emergency department care
Bibliographic record
Abstract
Objective The Ottawa Ankle Rules (OAR) is an evidence-based clinical decision tool used to predict fractures and need for radiographs in foot and ankle injury. Prior studies show that its use by both physicians and non-physicians reduces unnecessary radiographs and emergency department (ED) length of stay. This study evaluated the effect of triage nurse-led application of OAR on pain and satisfaction with ED care in patients with acute ankle injury. Methods A quasi-experimental design was used. Data was collected from 96 participants using the numerical pain rating scale and the Brief Emergency Department Patient's Satisfaction Scale. The intervention group had 46 participants and the control group had 50 participants. Results There was no statistically significant difference in level of pain on arrival (U = 1028, p = 0.34) and on discharge (U = 991, p = 0.21) from the ED between the control and intervention group. There was a significant difference between the control and intervention group in overall satisfaction with care received in the ED (t = 5.60; p = 0.000). There was also a significant difference between the two groups in their satisfaction with: ED staff (t = 4.12; p = 0.00); ED environment (t = 4.00; p = 0.00); physician care (t = 4.60; p = 0.00); general patient satisfaction (t = 6.60; p = 0.00); and patient family satisfaction (t = 3.44; p = 0.001). Conclusion Triage nurse application of the OAR improved patient satisfaction with ED care, but did not lead to a significant reduction in acute ankle injury related pain.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".