Cannabis use as a risk factor for causing motor vehicle crashes: a prospective study
Bibliographic record
Abstract
AIM: We conducted a responsibility analysis to determine whether drivers injured in motor vehicle collisions who test positive for Δ-9-tetrahydrocannabinol (THC) or other drugs are more likely to have contributed to the crash than those who test negative. DESIGN: Prospective case-control study. SETTING: Trauma centres in British Columbia, Canada. PARTICIPANTS: Injured drivers who required blood tests for clinical purposes following a motor vehicle collision. MEASUREMENTS: Excess whole blood remaining after clinical use was obtained and broad-spectrum toxicology testing performed. The analysis quantified alcohol and THC and gave semiquantitative levels of other impairing drugs and medications. Police crash reports were analysed to determine which drivers contributed to the crash (responsible) and which were 'innocently involved' (non-responsible). We used unconditional logistic regression to determine the likelihood (odds ratio: OR) of crash responsibility in drivers with 0 < THC < 2 ng/ml, 2 ng/ml ≤ THC < 5 ng/ml and THC ≥ 5 ng/ml (all versus THC = 0 ng/ml). Risk estimates were adjusted for age, sex and presence of other impairing substances. FINDINGS: We obtained toxicology results on 3005 injured drivers and police reports on 2318. Alcohol was detected in 14.4% of drivers, THC in 8.3%, other drugs in 8.9% and sedating medications in 19.8%. There was no increased risk of crash responsibility in drivers with THC < 2 ng/ml or 2 ≤ THC < 5 ng/ml. In drivers with THC ≥ 5 ng/ml, the adjusted OR was 1.74 [95% confidence interval (CI) = 0.59-6.36; P = 0.35]. There was significantly increased risk of crash responsibility in drivers with blood alcohol concentration (BAC) ≥ 0.08% (OR = 6.00;95% CI = 3.87-9.75; P < 0.01), other recreational drugs detected (OR = 1.82;95% CI = 1.21-2.80; P < 0.01) or sedating medications detected (OR = 1.45; 95%CI = 1.11-1.91; P < 0.01). CONCLUSIONS: In this sample of non-fatally injured motor vehicle drivers in British Columbia, Canada, there was no evidence of increased crash risk in drivers with Δ-9-tetrahydrocannabinol < 5 ng/ml and a statistically non-significant increased risk of crash responsibility (odds ratio = 1.74) in drivers with Δ-9-tetrahydrocannabinol ≥ 5 ng/ml.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".