Prehospital notification for major trauma patients requiring emergency hospital transport: A systematic review
Bibliographic record
Abstract
OBJECTIVE: This systematic review aimed to determine the effect of prehospital notification systems for major trauma patients on overall (<30 days) and early (<24 hours) mortality, hospital reception, and trauma team presence (or equivalent) on arrival, time to critical interventions, and length of hospital stay. METHODS: Experimental and observational studies of prehospital notification compared with no notification or another type of notification in major trauma patients requiring emergency transport were included. Risk of bias was assessed using the Cochrane ACROBAT-NRSI tool. A narrative synthesis was conducted and evidence quality rated using the GRADE criteria. RESULTS: Three observational studies of 72,423 major trauma patients were included. All were conducted in high-income countries in hospitals with established trauma services, with two studies undertaking retrospective analysis of registry data. Two studies reported overall mortality, one demonstrating a reduction in mortality; (adjusted odds ratio (OR) 0.61, 95% confidence interval (CI) 0.39 to 0.94, 72,073 participants); and the other demonstrating a nonsignificant change (OR 0.61, 95% CI 0.23 to 1.64, 81 participants). The quality of this evidence was rated as very low. CONCLUSION: Limited research on the topic constrains conclusive evidence on the effect of prehospital notification on patient-centered outcomes after severe trauma. Composite interventions that combine prehospital notification with effective actions on arrival to hospital such as trauma bay availability, trauma team presence, and early access to definitive management may provide more robust evidence towards benefits of early interventions during trauma reception and resuscitation.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | medium |
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 | 0.010 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".