Alcohol is a risk factor for helmet non-use and fatalities in off-road vehicle and motorcycle crashes
Bibliographic record
Abstract
Abstract Objectives: Off-road vehicle (ORV) and motorcycle use is common in Canada; however, risk of serious injury is heightened when these vehicles are operated without helmets and under the influence of alcohol. This study evaluated the impact of alcohol intoxication on helmet non-use and mortality among ORV and motorcycle crashes. Methods: Using data collected from the Nova Scotia Trauma Registry, a retrospective analysis (2002-2018) of ORV and motorcycle crashes resulting in major traumatic brain injury was performed. Patients were grouped by blood alcohol concentration (BAC) as negative (<2 mmol/L), legally intoxicated (2-17.3 mmol/L) or criminally intoxicated (>17.3 mmol/L). Logistic regression models were constructed to test for helmet non-use and mortality. Results: A total of 424 trauma patients were included in the analysis (220 ORV, 204 motorcycle). Less than half (45%) of patients involved in ORV crashes were wearing helmets and 65% were criminally intoxicated. Most patients involved in motorcycle crashes were helmeted at time of injury (88.7%) and 18% were criminally intoxicated. Those with criminal levels of intoxication had 3.7 times the odds of being unhelmeted and were 3 times more likely to die prehospital compared to BAC negative patients. There were significantly increased odds of in-hospital mortality among those with both legal (OR = 5.63), and criminal intoxication levels (OR = 4.97) compared to patients who were BAC negative. Conclusion: Alcohol intoxication is more frequently observed in ORV versus motorcycle crashes. Criminal intoxication is associated with helmet non-use. Any level of intoxication is a predictor of increased 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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".