Interventions to Reduce Imaging in Children With Minor Traumatic Head Injury: A Systematic Review
Bibliographic record
Abstract
CONTEXT: Reducing unnecessary imaging in emergency departments (EDs) for children with minor traumatic brain injuries (mTBIs) has been encouraged. OBJECTIVE: Our objective was to systematically review the effectiveness of interventions to decrease imaging in this population. DATA SOURCES: Eight electronic databases and the gray literature were searched. STUDY SELECTION: Comparative studies assessing ED interventions to reduce imaging in children with mTBIs were eligible. DATA EXTRACTION: Two independent reviewers screened studies, completed a quality assessment, and extracted data. The median of relative risks with interquartile range (IQR) are reported. A multivariable metaregression identified predictors of relative change in imaging. RESULTS: Twenty-eight studies were included, and most (79%) used before-after designs. The Pediatric Emergency Care Applied Research Network (PECARN) rule was the most common intervention (71%); most studies (75%) used multifaceted interventions (median components: 3; IQR: 1.75 to 4). Before-after studies assessing multi-faceted PECARN interventions reported decreased computed tomography (CT) head imaging (relative risk = 0.73; IQR: 0.60 to 0.89). Higher baseline imagine (P < .001) and additional intervention components (P = .008) were associated with larger imaging decreases. LIMITATIONS: The limitations of this study include the inconsistent reporting of important outcomes and that the results are based on non-randomized studies. CONCLUSIONS: Implementing interventions in EDs with high baseline CT ordering using complex interventions was more likely to reduce head imaging in children with mTBIs. Including the PECARN decision rule in the intervention strategy decreased orders by a median of 27%. Further research could provide insight into which specific factors influence successful implementation and sustained effects.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".