Effectiveness of Prehospital Critical Care Scene Response for Major Trauma: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: Major trauma is a leading cause of morbidity and mortality worldwide. It is unclear if the addition of a critical care response unit (CCRU) with capabilities comparable to hospital emergency departments might improve outcomes following major trauma, when added to Basic or Advanced Life Support (BLS/ALS) prehospital care. This systematic review describes the evidence for a CCRU scene response model for major trauma. METHODS: We searched Medline (Ovid), Embase (Ovid), Cochrane Central Register of Controlled Trials (Ovid), CINAHL (EBSCOhost), Science Citation Index Expanded (Web of Science), Conference Proceedings Citation Index - Science (Web of Science), LILACS (Latin American and Caribbean Health Sciences Literature) for relevant publications from 2003 to 2024. We included any study that compared CCRU and BLS/ALS care at the scene of major trauma, reported patient-focused outcomes, and utilized statistical methods to reduce bias and confounding. The risk of bias was assessed by two independent reviewers, using the ROBINS-I tool. Based on our a priori knowledge of the literature, a narrative analysis was chosen. The review was prospectively registered (PROSPERO ID CRD42023490668). RESULTS: The search yielded 5243 unique records, of which 26 retrospective cohort studies and one randomized controlled trial met inclusion criteria. Sample sizes ranged from 308 to 153,729 patients. Eighteen of the 27 included studies showed associations between CCRUs and improved survival following trauma, which appear to be more consistently found in more critically injured and adult patients, as well as those suffering traumatic cardiac arrest. The remaining nine studies showed no significant difference in outcomes between CCRU and BLS/ALS care. Most studies demonstrated critical or severe risks of bias. CONCLUSIONS: Current evidence examining CCRU scene response for major trauma suggests potential benefits in severely injury patients but is limited by overall low quality. Further high-quality research is required to confirm the benefits from CCRU scene response for major trauma.
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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 | 0.013 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| 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".