Impact of Trauma System Structure on Injury Outcomes: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
BACKGROUND: The effectiveness of trauma systems in decreasing injury mortality and morbidity has been well demonstrated. However, little is known about which components contribute to their effectiveness. We aimed to systematically review the evidence of the impact of trauma system components on clinically important injury outcomes. METHODS: We searched MEDLINE, EMBASE, Cochrane CENTRAL, and BIOSIS/Web of Knowledge, gray literature and trauma association Web sites to identify studies evaluating the association between at least one trauma system component and injury outcome. We calculated pooled effect estimates using inverse-variance random-effects models. We evaluated quality of evidence using GRADE criteria. RESULTS: We screened 15,974 records, retaining 41 studies for qualitative synthesis and 19 for meta-analysis. Two recommended trauma system components were associated with reduced odds of mortality: inclusive design (odds ratio [OR] = 0.72 [0.65-0.80]) and helicopter transport (OR = 0.70 [0.55-0.88]). Pre-Hospital Advanced Trauma Life Support was associated with a significant reduction in hospital days (mean difference [MD] = 5.7 [4.4-7.0]) but a nonsignificant reduction in mortality (OR = 0.78 [0.44-1.39]). Population density of surgeons was associated with a nonsignificant decrease in mortality (MD = 0.58 [-0.22 to 1.39]). Trauma system maturity was associated with a significant reduction in mortality (OR = 0.76 [0.68-0.85]). Quality of evidence was low or very low for mortality and healthcare utilization. CONCLUSIONS: This review offers low-quality evidence for the effectiveness of an inclusive design and trauma system maturity and very-low-quality evidence for helicopter transport in reducing injury mortality. Further research should evaluate other recommended components of trauma systems and non-fatal outcomes and explore the impact of system component interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.042 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".