The impact of short prehospital times on trauma center performance benchmarking
Bibliographic record
Abstract
BACKGROUND: Emergency medical service (EMS) prehospital times vary between regions, yet the impact of local prehospital times on trauma center (TC) performance is unknown. To inform external benchmarking efforts, we explored the impact of EMS prehospital times on the risk-adjusted rate of emergency department (ED) death and overall hospital mortality at urban TCs across the United States. METHODS: We used a novel ecologic study design, linking EMS data from the National EMS Information System to TCs participating in the American College of Surgeons' Trauma Quality Improvement Program (TQIP) by destination zip code. This approach provided EMS times for populations of injured patients transported to TQIP centers. We defined the exposure of interest as the 90th percentile total prehospital time (PHT) for each TC. TCs were then stratified by PHT quartile. Analyses were limited to adult patients with severe blunt or penetrating trauma, transported directly by land to urban TQIP centers. Random-intercept multilevel modeling was used to evaluate the risk-adjusted relationship between PHT quartile and the outcomes of ED death and overall hospital mortality. RESULTS: During the study period, 119,740 patients met inclusion criteria at 113 TCs. ED death occurred in 1% of patients, and overall mortality was 7.2%. Across all centers, the median PHT was 61 minutes (interquartile range, 53-71 minutes). After risk adjustment, TCs in regions with the shortest quartile of PHTs (<53 minutes) had significantly greater odds of ED death compared with those with the longest PHTs (odds ratio, 2.00; 95% confidence interval, 1.43-2.78). However, there was no association between PHT and overall TC mortality. CONCLUSION: At urban TCs, local EMS prehospital times are a significant predictor of ED death. However, no relationship exists between prehospital time and overall TC risk-adjusted mortality. Therefore, there is no evidence for the inclusion of EMS prehospital time in external benchmarking analyses.
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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 | 0.017 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".