Effect of triage nurse‐led application of the Ottawa Ankle Rules on number of radiographic tests and length of stay in selected emergency departments in Oman
Bibliographic record
Abstract
AIM: Ankle injuries are commonly seen in the emergency department (ED) and contribute to overcrowding. In Oman, injuries are a leading cause of years of life lost, disability-adjusted life years, and pose a burden to the healthcare system. This study aimed to evaluate the effectiveness of ED triage nurse-led application of the Ottawa Ankle Rules (OARs) toward improving the healthcare outcomes of ankle injury patients. METHODS: A quasi-experimental design was used to collect data (demographic characteristics, waiting time, length of stay, and number of radiographic tests) from 96 patients. The intervention group (n = 46) received ED triage nurse-led assessment and initiation of radiographic tests based on the OARs. The control group (n = 50) received usual care. RESULTS: The participants' mean age was 26.4 ± 7.90 years. The main causes of ankle injuries were football (36%), falls (31%) and twisting while walking (24%). There was a significant difference in number of ankle X-rays (t = 6.19; p < .001); length of stay (U = 549; p < .001); and waiting time (U = 167; p < .001) between the control and intervention group. The intervention reduced the mean waiting time and length of stay by 25.09 and 41.01 min, respectively. CONCLUSION: Application of the OARs by the ED triage nurse can decrease the number of unnecessary radiographic tests, waiting time and length of stay in the ED. Nurses' utilization of evidence-based clinical decision-making tools can improve ED care outcomes of common acute conditions such as ankle injuries.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".