Acute Effects of Cannabis on Alcohol Craving and Consumption: A Randomized Controlled Crossover Trial
Bibliographic record
Abstract
OBJECTIVE: Cannabis use is strongly linked with heavy drinking and worse alcohol treatment outcomes; however, it may also contribute to decreased alcohol consumption. To date, no human studies have established a causal effect of cannabis on alcohol motivation. The aim of this double-blind crossover randomized clinical trial was to examine dose-dependent acute effects of delta-9-tetrahydrocannabinol (THC) on alcohol craving and consumption. METHODS: Across three experimental days, 157 participants reporting heavy alcohol use and cannabis use two or more times weekly were randomized to smoke cannabis cigarettes containing 7.2% THC, 3.1% THC, or 0.03% THC (placebo), followed by exposures to neutral and personalized alcohol cues and an alcohol choice task for alcohol self-administration. A total of 138 participants completed two or more experimental sessions (mean age, 25.6 years [SD=5.1]; 35% women; 45% racial/ethnic minorities). Primary outcomes included craving, Alcohol Craving Questionnaire-Short Form, Revised (ACQ-SF-R), and an alcohol urge question; the secondary outcome was percent of total available milliliters of alcohol consumed. RESULTS: There were no significant effects of cannabis on ACQ-SF-R ratings after smoking and during alcohol cue exposure, but 7.2% THC reduced alcohol urge immediately after smoking. Participants consumed significantly less alcohol after smoking cannabis with 3.1% THC and 7.2% THC, reducing consumption by 19% and 27%, respectively. CONCLUSIONS: Following overnight cannabis abstinence, smoking cannabis acutely decreased alcohol consumption compared to placebo. Further controlled research on a variety of cannabinoids is needed to inform clinical alcohol treatment guidelines.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".