Cannabis use and driving-related performance in young recreational users: a within-subject randomized clinical trial
Bibliographic record
Abstract
Background: With the legalization of cannabis in Canada, young adults, who are already at risk of automobile crashes, may increase their use of cannabis, which may further increase the risk of crashes. We examined the effects of inhaled cannabis on driving-related performance in healthy 18- to 24-year-old recreational cannabis users. Methods: In this within-subject randomized study, participants completed tests in the no-cannabis state and at 1, 3 and 5 hours after inhalation of a standard 100-mg dose of cannabis. We then measured performance (in useful-field-of-view and driving-simulation tests) and self-reported perceptions (driving ability and safety, cannabis effects). Repeated-measures analysis of variance (for cannabis effects on continuous performance measures), Cochran Q tests (for performance-related crash risk and binary complex simulator task scores) and correlational analyses (for self-reported perceptions relative to performance) were employed. Results: Forty-five participants completed all 180 testing sessions. Significant effects of cannabis (relative to no cannabis) were noted on complex useful-field-of-view tasks at 3 hours (complex divided-attention task: 70 ± 24 ms v. 37 ± 12 ms, 95% confidence intervals [CIs] 28–114 ms v. 29–45 ms, t = −2.98, df = 41, p = 0.005; complex selective-attention task: 102 ± 66 ms v. 64 ± 18 ms, 95% CIs 60–144 ms v. 53–75 ms, t = −2.42, df = 41, p = 0.02) and 5 hours (complex selective-attention task: 82 ± 29 ms v. 61 ± 19 ms, 95% CIs 62–100 ms v. 48–75 ms, t = −2.32, df = 41, p = 0.03) after cannabis use when the tasks were novel (performed in a cannabis state at the first session). Participants were significantly more likely to be classified as having a high crash risk (on the basis of simulator tasks) after cannabis use (χ2 = 13.23, df = 1, p < 0.001, odds ratio 4.31, 95% CI 0.41–45.2) and reported significantly lower perceived driving ability and safety after cannabis use relative to non-use. Interpretation: Among young recreational cannabis users, a 100-mg dose of cannabis by inhalation had no effect on simple driving-related tasks, but there was significant impairment on complex tasks, especially when these were novel. These effects, along with lower self-perceived driving ability and safety, lasted up to 5 hours after use. Trial registration: The trial was registered with Health Canada (NOL [No Objection Letter] no. 215101).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".