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 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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".