Short-term neuropsychological outcome following uncomplicated mild TBI: Effects of day-of-injury intoxication and pre-injury alcohol abuse.
Bibliographic record
Abstract
Research suggests that individuals who are intoxicated at the time of traumatic brain injury (TBI) have worse cognitive outcome compared with those who are sober. Worse outcome in patients with day-of-injury intoxication might (a) be related to the increased magnitude of brain injury resulting from a variety of negative responses not present following TBI in nonintoxicated individuals, or (b) reflect the effect of pre-injury alcohol abuse that is prevalent in individuals intoxicated at the time of injury. Most studies in this area have focused on patients with moderate to severe TBIs, and on medium- to long-term neuropsychological outcome. The purpose of this study was to examine the relative contributions of day-of-injury intoxication versus pre-injury alcohol abuse on short-term cognitive recovery following mild TBI. Participants were 169 patients with uncomplicated mild TBIs who were assessed on 13 cognitive measures within 7 days postinjury. The prevalence of intoxication at the time of injury was 54.4%. The prevalence of possible pre-injury alcohol abuse was 46.2%. Overall, the results suggest that pre-injury alcohol abuse, compared with day-of-injury alcohol intoxication, had the most influence on short-term neuropsychological outcome from uncomplicated mild TBI. However, the influence of pre-injury alcohol abuse was considered small at best.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".