Alcohol and mortality after moderate to severe traumatic brain injury: a meta-analysis of observational studies
Bibliographic record
Abstract
OBJECT Experimental studies have shown numerous neuroprotective properties of alcohol ("ethanol") after TBI, but clinical studies have provided conflicting results. The authors aimed to assess the relationship between positive blood alcohol concentration (BAC) on hospital admission and mortality after moderate to severe traumatic brain injury (TBI). METHODS The authors searched 8 databases for observational studies reported between January 1, 1990, and October 7, 2013, and investigated the effect of BAC on mortality after moderate to severe TBI. Reviews of each study were conducted, and data were extracted according to the MOOSE and PRISMA guidelines. Study quality was assessed using the Newcastle-Ottawa scale. The Mantel-Haenszel fixed effect methodology was used to generate pooled estimates. Heterogeneity was dealt with by multiple sensitivity analyses. RESULTS Eleven studies with a total of 95,941 patients (42% BAC positive and 58% BAC negative) were identified for the primary analysis (overall mortality 12%). Primary analysis showed a significantly lower risk of death for BAC-positive patients compared with BAC-negative patients (crude mortality 11.0% vs 12.3%, pooled OR 0.84 [95% CI 0.81-0.88]), although flawed by heterogeneity (I(2) = 68%). Multiple sensitivity analyses, including 55,949 and 51,772 patients, yielded similar results to the primary analysis (crude mortality 12.2% vs 14.0%, pooled OR 0.87 [95% CI 0.83-0.92] and crude mortality 8.7% vs 10.7%, pooled OR 0.78 [95% CI 0.74-0.83]) but with good study homogeneity (I(2) = 36% and 14%). CONCLUSIONS Positive BAC was significantly associated with lower mortality rates in moderate to severe TBI. Whether this observation is due to selection bias or neuroprotective effects of alcohol remains unknown. Future prospective studies adjusting for TBI heterogeneity is advocated to establish the potential favorable effects of alcohol on outcome after TBI.
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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 | 0.022 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.059 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".