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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".