Blood alcohol concentration as a determinant of outcomes after traumatic spinal cord injury
Bibliographic record
Abstract
BACKGROUND: Pre-clinical studies indicate a potential detrimental effect of ethanol on tissue sparing and locomotor recovery in animal models of spinal cord injury (SCI). Given this, an examination of whether blood alcohol concentration (BAC) is a potential determinant of survival and neurological and functional recovery after acute traumatic SCI was carried out. METHODS: All patients who were enrolled in the Third National Spinal Cord Injury Study (NASCIS-3) were included. The study population was divided into 'non-alcohol' (BAC equal to 0‰), 'legal' (BAC greater than 0 up to 0.8‰) and 'illegal' (BAC greater than 0.8‰) subgroups. Outcome measures included survival, NASCIS motor and sensory scores, NASCIS pain scores and Functional Independence Measure (FIM) determinants at baseline and at 6 weeks, 6 months and 1 year post-SCI. Analyses were adjusted for major potential confounders: age, sex, ethnicity, trial protocol, Glasgow coma score, and cause, level and severity of SCI. RESULTS: Among 499 patients (423 males and 76 females; ages from 14 to 92 years), the mean BAC was 0.054 ± 0.006‰ (range 0-1). The survival at 1 year (94.4%) was not associated with the BAC (P = 0.374). Moreover, BAC was not significantly correlated with motor recovery (P > 0.166), sensory recovery (P > 0.323), change in pain score (P > 0.312) or functional recovery (P > 0.133) at 6 weeks, 6 months and 1 year post-SCI. CONCLUSIONS: Our results, for the first time, show that the BAC at emergency admission does not adversely affect the patients' mortality, neurological impairment or functional disability over the course of the first year after SCI.
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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 | 0.003 |
| 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 | 0.000 |
| 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".