[X-ray requests for minor limb injuries. A systematic review].
Bibliographic record
Abstract
X-ray requests for minor limb trauma: a systematic review. INTRODUCTION: Overcrowding in Emergency Department (ED) leads to an increased waiting time causing dissatisfaction both in patients and staff, in addition to possible negative events. AIM: To assess if the request of x-rays by triage nurses, for isolated injuries, during the waiting time before the physician assessment, might improve the flow of ED patient, affecting the lenght of stay (LOS) and the waiting time in the department. Nurses' satisfaction in addition to the accuracy of the requested x-rays was also assessed. METHODS: A systematic review was conducted according to the PRISMA method, questioning PUBMED and CINAHL databases, selecting full text articles from the year 1995 and considering only adult population. RESULTS: 14 studies were selected: 7 RCT, 5 observational studies, 1 pilot study and 1 quasi-experimental study. The request of x-rays by the triage nurses significantly improved the waiting and staying time in the ED together with the patients and staff's satisfaction. CONCLUSIONS: Anticipating the x-rays requests during triage can be a solution for reducing waiting and staying time in the ED. In addition, it impacts also on patients and nurses' satisfaction. The accuracy of x-rays is strictly linked to Nurses' education and the use of validated tools like Ottawa Ankle Rules, which had good feedback from the nursing staff.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".