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Record W4409087111 · doi:10.1702/4470.44680

[X-ray requests for minor limb injuries. A systematic review].

2025· review· en· W4409087111 on OpenAlexaboutno aff
Simone Lazzati, Paola Bosco, Marta Locoro, Monica Solbiati, Mauro Salvato

Bibliographic record

VenuePubMed · 2025
Typereview
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsnot available
Fundersnot available
KeywordsMinor (academic)Physical medicine and rehabilitationMedicinePsychologyPolitical science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.415
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.135
GPT teacher head0.465
Teacher spread0.330 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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