The mortality benefit of direct trauma center transport in a regional trauma system
Bibliographic record
Abstract
BACKGROUND: By ensuring timely access to trauma center (TC) care, well-organized trauma systems have the potential to significantly reduce injury-related mortality. However, undertriage continues to be a significant problem in many regional trauma systems. Taking a novel, population-based approach, we estimated the potential detrimental impact of undertriage to a non-TC (NTC) within a regional system. METHODS: We performed a population-based, retrospective cohort study of TC effectiveness in a region with urban, suburban, and rural areas. Data were derived from administrative databases capturing all emergency department deaths and admissions in the region. Adult motor vehicle collision occupants presenting to any emergency department in the study region were included (2002-2010). Data were limited to patients with severe injury. The exposure of interest was initial triage destination (TC or NTC), regardless of later transfer to TC. Mortality was compared across groups, using an instrumental variable analysis to adjust for confounding. RESULTS: Among 6,341 motor vehicle collision occupants, 45% (n = 2,857) were triaged from the scene of injury to a TC. Among patients transported from the scene to a NTC, 57% (n = 2,003) were transferred to a TC within 24 hours of initial evaluation. Compared with patients triaged to a NTC, adjusted mortality was lower among patients triaged directly to a TC, both at 24 hours (odds ratio: 0.58, 95% confidence interval: 0.41-0.84) and at 48 hours (odds ratio: 0.68, 95% confidence interval: 0.48-0.96). A trend toward reduced mortality with TC triage was also observed at 7 and 30 days. CONCLUSIONS: Our data are population-based evidence of the early benefits of direct triage to TC. Although many surviving patients are later transferred to a TC, initial triage to a NTC is associated with at least a 30% increase in mortality in the first 48 hours after injury. LEVEL OF EVIDENCE: Therapeutic study, level IV.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".