Effects of Cannabis Alone and Combined with Alcohol on Simulated Driving
Bibliographic record
Abstract
Alcohol and cannabis remain the two most commonly detected drugs in injured drivers. The simultaneous use of these drugs is commonly reported and their combined use increases the risk of impaired driving. Additionally, the potency of cannabis for recreational use has risen causing a discrepancy between the potency being sold, and that used in driving research. Study 1 examined the effects of combinations of smoked cannabis (12.5% THC) and alcohol (BrAC 0.08%) on simulated driving performance, subjective drug effects, cardiovascular measures and self-reported perception of driving ability in 28 youngadults. The combined use of alcohol and cannabis increased weaving and reaction time, and tended to produce greater subjective effects compared to placebo and the singledrug conditions suggesting a potential additive effect. Furthermore, participants seemed to be unaware of their greater level of impairment when under the influence of both drugs. Study 2 examined the effects of four different doses of smoked cannabis: placebo (0.009% THC), low dose (6.25% THC), medium dose (12.5% THC), and high dose (22% THC) on simulated driving performance and subjective drug effects in 18 adults. At the time that this analysis was conducted, the clinical trial was still ongoing. In order not to break the blind, Pharmacy services at the Centre for Addiction and Mental Health designated the four drug conditions as A, B, C and D. Conditions A, B, and C showed increased weaving and reaction time at the 30-minute drive post drug administration and increased weaving at the 90-minute drive post drug administration compared to condition D. Condition C led to significantly greater weaving in the 30-minute drive compared to condition B and D, suggesting a dose-response effect. In line with these findings, the subjective drug effects were greater in condition A, B and C compared to condition D. ConclusionThe results of these studies add to the existing literature highlighting the increased driving impairment when cannabis and alcohol are used simultaneously. Individuals driving under these conditions do not seem to be aware of their increased impairment. The findings also suggest that driving impairment under the influence of cannabis maybe dose-related.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".