Cannabidiol as a treatment for craving and relapse in individuals with cocaine use disorder: a randomized placebo‐controlled trial
Bibliographic record
Abstract
BACKGROUND AND AIMS: Cocaine use disorder (CUD) is a significant public health concern for which no efficacious pharmacological interventions are available. Cannabidiol (CBD) has attracted considerable interest as a promising treatment for addiction. This study tested CBD efficacy for reducing craving and preventing relapse in people with CUD. DESIGN: Single-site double-blind randomized controlled superiority trial comparing CBD with placebo. SETTING AND PARTICIPANTS: Centre Hospitalier de l'Université de Montréal, Canada. Seventy-eight adults (14 women) with moderate to severe CUD participated. INTERVENTION: Participants were randomly assigned (1 : 1) by stratified blocks to daily 800 mg CBD (n = 40) or placebo (n = 38). They first underwent an inpatient detoxification phase lasting 10 days. Those who completed this phase entered a 12-week outpatient follow-up. MEASUREMENTS: Primary outcomes were drug-cue-induced craving during detoxication and time-to-cocaine relapse during subsequent outpatient treatment. FINDINGS: During drug-cue exposure, craving scores [mean ± standard deviation (SD)] increased from baseline by 4.69 (2.89) versus 3.21 (2.78) points, respectively, in CBD (n = 36) and placebo (n = 28) participants [confidence interval (CI) = -0.33 to 3.04; P = 0.069; Bayes factor = 0.498]. All but three participants relapsed to cocaine by week 12 with similar risk for CBD (n = 34) and placebo (n = 27) participants (hazard ratio = 1.20, CI = 0.65-2.20, P = 0.51; Bayes factor = 0.152). CBD treatment was well tolerated and associated mainly with diarrhoea. CONCLUSIONS: CBD did not reduce cocaine craving or relapse among people being treated for CUD.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".