Impact of ICU Structure and Processes of Care on Outcomes After Severe Traumatic Brain Injury: A Multicenter Cohort Study
Bibliographic record
Abstract
OBJECTIVES: It is uncertain whether dedicated neurocritical care units are associated with improved outcomes for critically ill neurologically injured patients in the era of collaborative protocol-driven care. We examined the association between dedicated neurocritical care units and mortality and the effects of standardized management protocols for severe traumatic brain injury. DESIGN: We surveyed trauma medical directors from centers participating in the American College of Surgeons Trauma Quality Improvement Program to obtain information about ICU structure and processes of care. Survey data were then linked to the Trauma Quality Improvement Program registry, and random-intercept hierarchical multivariable modeling was used to evaluate the association between dedicated neurocritical care units, the presence of standardized management protocols and mortality. SETTING: Trauma centers in North America participating in Trauma Quality Improvement Program. PATIENTS: Data were analyzed from 9,773 adult patients with isolated severe traumatic brain injury admitted to 134 Trauma Quality Improvement Program centers between 2011 and 2013. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Only 50 ICUs (37%) were dedicated neurocritical care units, whereas 84 (63%) were general ICUs. Rates of standardized management protocols were similar comparing dedicated neurocritical care units and general ICUs. Among severe TBI patients admitted to trauma centers enrolled in Trauma Quality Improvement Program, care in a dedicated neurocritical care unit did not improve risk-adjusted in-hospital survival (odds ratio, 0.97; 95% CI, 0.80-1.19; p = 0.79). However, the presence of a standardized management protocol for these patients was associated with lower risk-adjusted in-hospital mortality (odds ratio, 0.77; 95% CI, 0.63-0.93; p = 0.009). CONCLUSIONS: Compared with dedicated neurocritical care models, standardized management protocols for severe traumatic brain injured patients are process-targeted intervention strategies that may improve clinical outcomes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".