Effects of cannabidiol on alcohol-related outcomes: A review of preclinical and human research.
Bibliographic record
Abstract
Increased access to medicinal and recreational cannabis will be accompanied by greater exposure to its chemical constituents, including Δ9-tetrahydrocannabinol (Δ9-THC) and cannabidiol (CBD), the primary nonpsychoactive compound. Increasing attention has focused on CBD, in part, due to its potential therapeutic properties. Relatively little is known about how CBD might interact with other commonly used drugs. While a number of studies have explored the influence of cannabis or Δ9-THC on alcohol consumption and treatment outcomes, few have examined the effects of CBD on alcohol-related outcomes. This article reviews preclinical and human studies examining the effects of CBD administration on alcohol responses. Preliminary preclinical results suggest that CBD can attenuate alcohol consumption and potentially protect against certain harmful effects of alcohol, such as liver and brain damage. Also reviewed herein are the few existing studies involving CBD and alcohol coadministration in humans. The paucity of such studies precludes any definitive conclusions relating to CBD-alcohol interactions. Effects of CBD on alcohol use and potential therapeutic implications for alcohol use disorder are discussed. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".