Patterns of Head CT Utilization in Emergency Department Patients With Minor Head Injury: A Systematic Review
Bibliographic record
Abstract
Minor head injury (MHI) is a frequent presentation to emergency departments (EDs), and while most patients recover uneventfully, a small proportion develop clinically important traumatic brain injury (ciTBI). Computed tomography (CT) is the diagnostic gold standard for detecting intracranial pathology, but its widespread use contributes to unnecessary radiation exposure, higher costs, and ED crowding. To optimize utilization, several clinical decision rules, including the Canadian CT Head Rule, New Orleans Criteria, National Emergency X-Radiography Utilization Study II (NEXUS-II), and National Institute for Health and Care Excellence (NICE) guidelines, have been developed to balance sensitivity for ciTBI with the need to limit avoidable scans. This systematic review, conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, searched PubMed, Embase, Scopus, and the Cochrane Library up to August 2025. Of the 96 records screened, five studies with over 44,000 patients met the inclusion criteria. The findings indicate that although decision rules demonstrate high sensitivity and strong potential to reduce unnecessary imaging, variability in adherence leads to both overuse and underuse of CT. Greater integration of validated rules into clinical workflows and decision-support systems is needed to enhance patient safety, reduce costs, and improve efficiency in the management of MHI.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".