Alcohol Consumption Does not Impede Recovery from Mild to Moderate Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVES: To examine the effect of pre-injury alcohol use, acute alcohol intoxication, and post-injury alcohol use on outcome from mild to moderate traumatic brain injury (TBI). METHODS: Prospective inception cohort of patients who presented to the Emergency Department with mild to moderate TBI and had a blood alcohol level (BAL) taken for clinical purposes. Those who completed the 1-year outcome assessment were eligible for this study (N=91). Outcomes of interest were the count of post-concussion symptoms (British Columbia Post-Concussion Symptom Inventory), low neuropsychological test scores (Neuropsychological Assessment Battery), and abnormal regions of interest on diffusion tensor imaging (low fractional anisotropy). The main predictors were pre-injury alcohol consumption (Cognitive Lifetime Drinking History interview), BAL, and post-injury alcohol use. RESULTS: The alcohol use variables were moderately to strongly inter-correlated. None of the alcohol use variables (whether continuous or categorical) were related to 1-year TBI outcomes in generalized linear modeling. Participants in this cohort generally had a good clinical outcome, regardless of their pre-, peri-, and post-injury alcohol use. CONCLUSIONS: Alcohol may not significantly alter long-term outcome from mild to moderate TBI. (JINS, 2016, 22, 816-827).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".