Good Neighbors? The Effect of a Level 1 Trauma Center on the Performance of Nearby Level 2 Trauma Centers
Bibliographic record
Abstract
OBJECTIVE: In this study, we sought to determine whether the proximity of a level 1 trauma center (TC) might affect the performance of a nearby level 2 TC. BACKGROUND: With the exception of research and teaching programs, level 2 TC must function at a level similar to that of level 1 TC, and provide high quality, definitive care to severely injured patients. However, the role of a level 2 TC within a region might vary significantly depending on the local trauma care environment. We postulated that the case mix, regional role and outcomes of level 2 TC are greatly influenced by the regional presence of a level 1 TC. METHODS: Data were derived from the National Trauma Databank (9.0), limiting to adults with Injury Severity Score ≥9. Level 2 TC were classified as either isolated trauma centers (ITC, >30 miles from the closest level 1 TC) or neighbored trauma centers (NTC, ≤30 miles from the closest level 1 TC). Regression was used to calculate risk-adjusted mortality at each center type. RESULTS: Fifty-five thousand six hundred and fifty-five patients were identified at 161 centers; 55% of patients were cared for at ITC (n = 84 centers). Case mix varied significantly across center type; in particular, ITC received significantly more transfer patients than NTC. After adjusting for differences in case mix, patients at ITC had a 12% lower risk of death than patients treated at NTC (0.88, 95% CI 0.78-0.98). CONCLUSIONS: Level 2 TC assume different roles depending on the local trauma system configuration. Ideally, a level 2 TC should benefit from the presence of a nearby level 1 TC through collaborations in care protocols and shared case reviews. However, these data suggest the opposite: level 2 centers in proximity to level 1 centers might perform at a lower than expected level.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".