Co-exposure of cocaine and cannabinoids and its association with select biological, behavioural and health outcomes: A systematic scoping review of multi-disciplinary studies
Bibliographic record
Abstract
Cocaine use entails severe health- and social-related harms globally. Treatment options for cocaine dependence are highly limited. Benefits of cannabinoids for addiction have been documented, making it opportune to examine existing data on the possible outcomes associated with cannabinoids and cocaine co-use. We conducted a systematic scoping review following the PRISMA guidelines of peer-reviewed, English-language studies published from 2000 to 2021 in four databases (Medline, Web-of-Science, CINAHL Plus, and PsycInfo), assessing the co-exposure of cannabis/cannabinoids with cocaine on behavioural, biological or health outcomes. Both quantitative and qualitative, as well as humans and pre-clinical animals' studies (n=46) were included. Pre-clinical studies (n=19) showed mostly protective effects of cannabidiol (CBD) administration on animal models of addiction (e.g., cocaine-craving, -relapse, and -withdrawal) and cocaine-toxicity. Tetrahydrocannabinol (THC) had more inconsistent results, with both protective and counter-protective effects. Human studies (n=27) were more heterogeneous and assessed natural ongoing cannabis and cocaine use or dependence. Quantitative-based studies showed mostly enhanced harms in several outcomes (e.g., cocaine use, mental health); two available clinical trials found no effect upon CBD administration on cocaine-related treatment outcomes. Qualitative data-based studies reported cannabis use as a substitute for or to alleviate harms of crack-cocaine use. While pre-clinical studies suggest a potential of cannabinoids, especially CBD, to treat cocaine addiction, the few trials conducted in humans found no benefits. Cannabis co-use by cocaine users commonly presents a risk factor, entailing enhanced harms for users. More rigorous, controlled trials are still necessary to investigate cannabinoids' potential considering pre-clinical findings and reported benefits from specific drug users.
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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 | 0.016 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.026 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".