Understanding Hospital Volume–Outcome Relationship in Severe Traumatic Brain Injury
Bibliographic record
Abstract
BACKGROUND: The hospital volume-outcome relationship in severe traumatic brain injury (TBI) population remains unclear. OBJECTIVE: To examine the relationship between volume of patients with severe TBI per hospital and in-hospital mortality, major complications, and mortality following a major complication (ie, failure to rescue). METHODS: In a multicenter cohort study, data on 9255 adults with severe TBI were derived from 111 hospitals participating in the American College of Surgeons Trauma Quality Improvement Program over 2009-2011. Hospitals were ranked into quartiles based on their volume of severe TBI during the study period. Random-intercept multilevel models were used to examine the association between hospital quartile of severe TBI volume and in-hospital mortality, major complications, and mortality following a major complication after adjusting for patient and hospital characteristics. In sensitivity analyses, we examined these associations after excluding transferred cases. RESULTS: Overall mortality was 37.2% (n = 3447). Two thousand ninety-eight patients (22.7%) suffered from 1 or more major complication. Among patients with major complications, 27.8% (n = 583) died. Higher-volume hospitals were associated with lower mortality; the adjusted odds ratio of death was 0.50 (95% confidence interval: 0.29-0.85) in the highest volume quartile compared to the lowest. There was no significant association between hospital-volume quartile and the odds of a major complication or the odds of death following a major complication. After excluding transferred cases, similar results were found. CONCLUSION: High-volume hospitals might be associated with lower in-hospital mortality following severe TBI. However, this mortality reduction was not associated with lower risk of major complications or death following a major complication.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".