Trauma system regionalization improves mortality in patients requiring trauma laparotomy
Bibliographic record
Abstract
INTRODUCTION: This study evaluates the impact of a regional trauma network (RTN) on patient survival, intensive care unit (ICU) length of stay, and hospital length of stay in patients who required trauma laparotomy. METHODS: Patients who required trauma laparotomy from January 2008 to December 2013 were analyzed. Patients admitted during 2008-2009 and 2011-2013 were designated as pre-RTN and RTN groups, respectively. The primary outcome was mortality. RESULTS: A total of 569 patients were analyzed, 231 patients were pre-RTN, and 338 were in the RTN group. Overall, mean age was 35.7 ± 17.1 and median Injury Severity Score was 16 (25th-75th percentile: 9-26). The two groups were similar with regard to age, Injury Severity Score, Abbreviated Injury Scale abdomen, sex, and mechanism. Overall, there was a 35% relative reduction in mortality from the pre-RTN to RTN group (p = 0.035), and 30% more patients were triaged to a Level 1 trauma center in the RTN group (p < 0.001). Logistic regression showed that being in the RTN group was an independent predictor for survival (p = 0.026) with odds ratio of 0.53 (95% confidence interval, 0.30-0.93). Patients with penetrating trauma had a nonsignificant decrease in mortality and a reduction of 1 day of ICU stay (p = 0.001). Patients with blunt trauma had a significant reduction in mortality from 38% in the pre-RTN group to 23% in the RTN group (p = 0.017). CONCLUSION: This study focused on the unique patient population that required trauma laparotomies. It showed that trauma system regionalization led to a significant increase in the number of patients triaged to a Level 1 trauma center and reduction of ICU length of stay. More importantly, it demonstrated the benefit of regionalization by showing a significant reduction of hospital mortality in this critically injured patient population. 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".