High‐‘n’‐dry? A comparison of cannabis and alcohol use in drivers presenting to hospital after a vehicular collision
Bibliographic record
Abstract
DESIGN: This was a prospective observational study. BACKGROUND AND AIMS: The characteristics of cannabis-involved motor vehicle collisions are poorly understood. This study of injured drivers identifies demographic and collision characteristics associated with high tetrahydrocannabinol (THC) concentrations. SETTING: The study was conducted in 15 Canadian trauma centres between January 2018 and December 2021. CASES: The cases (n = 6956) comprised injured drivers who required blood testing as part of routine trauma care. MEASUREMENTS: We quantified whole blood THC and blood alcohol concentration (BAC) and recorded driver sex, age and postal code, time of crash, crash type and injury severity. We defined three driver groups: high THC (THC ≥ 5 ng/ml and BAC = 0), high alcohol (BAC ≥ 0.08% and THC = 0) and THC/BAC-negative (THC = 0 = BAC). We used logistic regression techniques to identify factors associated with group membership. FINDINGS: Most injured drivers (70.2%) were THC/BAC-negative; 1274 (18.3%) had THC > 0, including 186 (2.7%) in the high THC group; 1161 (16.7%) had BAC > 0, including 606 (8.7%) in the high BAC group. Males and drivers aged less than 45 years had higher adjusted odds of being in the high THC group (versus the THC/BAC-negative group). Importantly, 4.6% of drivers aged less than 19 years had THC ≥ 5 ng/ml, and drivers aged less than 19 years had higher unadjusted odds of being in the high THC group than drivers aged 45-54 years. Males, drivers aged 19-44 years, rural drivers, seriously injured drivers and drivers injured in single-vehicle, night-time or weekend collisions had higher adjusted odds ratios (aORs) for being in the high alcohol group (versus THC/BAC-negative). Drivers aged less than 35 or more than 65 years and drivers involved in multi-vehicle, daytime or weekday collisions had higher adjusted odds for being in the high THC group (versus the high BAC group). CONCLUSIONS: In Canada, risk factors for cannabis-related motor vehicle collisions appear to differ from those for alcohol-related motor vehicle collisions. The collision factors associated with alcohol (single-vehicle, night-time, weekend, rural, serious injury) are not associated with cannabis-related collisions. Demographic factors (young drivers, male drivers) are associated with both alcohol and cannabis-related collisions, but are more strongly associated with cannabis-related collisions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".