Association Between Alcohol and Mortality in Patients With Severe Traumatic Head Injury
Bibliographic record
Abstract
HYPOTHESIS: Admission blood alcohol concentration (BAC) is associated with in-hospital death in patients with severe brain injury from blunt head trauma. DESIGN: Retrospective cohort study. SETTING: Academic level I trauma center in Toronto, Ontario. PATIENTS: Using trauma registry data, between January 1, 1988, and December 31, 2003, we identified 1158 consecutive patients with severe brain injury from blunt head trauma. INTERVENTION: There was no active intervention. The primary exposure of interest was the BAC at admission, stratified into the following 3 levels: 0, no BAC; 0 to less than 230 mg/dL, low to moderate BAC; and 230 mg/dL or greater, high BAC. MAIN OUTCOME MEASURE: In-hospital death. RESULTS: In patients with severe brain injury, low to moderate BAC was associated with lower mortality than was no BAC (27.9% vs 36.3%; P = .008). High BAC was associated with higher mortality than was no BAC (44.7% vs 36.3%), although this was not statistically significant (P = .10). These associations were all statistically significant after adjusting for demographic data and injury factors using logistic regression analysis. The odds ratio for death was 0.76 (95% confidence interval, 0.52-0.98) for low to moderate BAC compared with no BAC. The odds ratio for death was 1.73 (95% confidence interval, 1.05-2.84) for high BAC compared with no BAC. CONCLUSIONS: Low to moderate BAC may be beneficial in patients with severe brain injury from blunt head trauma. In contrast, high BAC seems to have a deleterious effect on in-hospital death in these patients, which may be related to its detrimental hemodynamic and physiologic effects. Alcohol-based fluids may have a role in the management of patients with severe brain injury after they have been well resuscitated.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".