Interventions to reduce imaging in children with upper or lower extremity injuries: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Radiation exposure, transition delays and costs associated with unnecessary imaging in children have stimulated research into clinical decision rules and other interventions to reduce imaging in the emergency department (ED). The objective of this systematic review is to examine the effectiveness of implementing interventions to reduce imaging in children with upper/lower extremity injuries in the ED. METHODS: Seven databases and the grey literature were searched up to May 2024. Comparative studies assessing interventions to reduce imaging in children with upper/lower extremity injuries implemented in the ED were eligible. Two independent reviewers screened for study eligibility, quality assessment and data extraction, with disagreements settled via third-party adjudication. Changes in imaging are reported as ORs with 95% CIs, using a random effects model. RESULTS: =38%). A decision rule for wrist injuries reduced imaging (OR=0.06; 95% CI 0.03 to 0.11); however, eight injuries were missed. Two studies implementing clinical guidelines reported decreases in radiographs per patient (p<0.001). One trial reported increased imaging in children assessed by triage nurses using an established clinical decision rule (OR=5.44; 95% CI 2.96 to 10.02), with 16 missed injuries identified. CONCLUSIONS: Guidelines incorporating clinical decision rules, particularly decision rules for ankle injuries, can reduce radiography for children with extremity injuries in the ED. Further investigations are warranted to identify other extremity injuries, the components of the intervention and the most efficient clinicians to target. PROSPERO REGISTRATION NUMBER: CRD42016042875.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".