Trauma Surge Index: Advancing the Measurement of Trauma Surges and Their Influence on Mortality
Bibliographic record
Abstract
BACKGROUND: Increases in trauma patient volume and acuity, such as during mass casualty events, can overwhelm hospitals, potentially worsening patient outcomes. Due to methodological limitations, the effect of trauma surges on clinical outcomes remains unclear, so hospitals have not prepared for such events in an evidence-based manner. The objective of this study was to develop a new measure of hospital capacity strain corresponding to trauma admissions and to examine the relationship between trauma surges and inpatient mortality. STUDY DESIGN: Using trauma registry data from hospitals across the United States and Canada (2010 to 2011), we developed the Trauma Surge Index (TSI), a measure of capacity strain that controls for variation in hospital admission volume and patient acuity. Using the TSI and an established definition of mass casualty events, we quantified hospital surges and entered each measure as an exposure variable in separate risk-adjusted mortality models. RESULTS: Using the TSI method, we observed that patients admitted during high-surge periods display significantly increased mortality compared with patients admitted during low-surge periods (odds ratio [OR] = 2.05; 95% CI, 1.36-3.10), and patients with firearms injuries were particularly at risk (OR = 7.29; 95% CI, 2.13-24.91). Using mass casualty event criteria, we found no difference between the mortality of patients admitted during trauma surges and nonsurge periods (OR = 0.94; 95% CI, 0.88-1.01). CONCLUSIONS: We demonstrate the TSI, which is a novel method that identified periods of high-capacity strain in hospitals associated with increased trauma patient mortality. Our newly developed TSI method can be implemented by hospitals and trauma systems to examine periods of high-capacity strain retrospectively, identify specific resources that might have been needed, and better direct future investments in an evidence-based manner.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".