P.184 Off-road vehicle fatalities and alcohol in patients with major traumatic brain injury: the risk of impaired driving
Bibliographic record
Abstract
Background: Intoxicated patients injured in off road vehicle (ORV) crashes have higher rates of traumatic brain injury (TBI) and intensive care unit (ICU) admission, as well as prolonged ICU length of stay. This study evaluated the impact of alcohol intoxication on mortality among major TBI patients injured in off-road vehicle crashes. Methods: A retrospective analysis (2002-2014) of off-road vehicle injuries in Nova Scotia resulting in major TBI was performed. ORVs included ATVs, snowmobiles, and dirt bikes. A logistic regression model was constructed to test for in-hospital mortality and adjusted for age, Abbreviated Injury Scale (AIS) Head, Injury Severity Score, and blood alcohol concentration (BAC). Results: There were 176 drivers and passengers of off-road vehicles. Overall mortality was 28%. BAC testing was performed in 61% patients; 85% of pre-hospital deaths were BAC positive (mean BAC=31 ± 17.39 mmol/L) and 70% in-hospital deaths were BAC positive (mean BAC=26 ± 23.12 mmol/L). After adjusting for confounders, high injury severity and intoxication increased the likelihood of in-hospital mortality. Conclusions: These findings demonstrate that alcohol intoxication is a significant risk factor for mortality among off-road vehicle collisions; for every mmol/L change in BAC, there was a 10% increase in the chance of in-hospital mortality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.011 | 0.002 |
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".