Psychosocial and pharmacological interventions for the treatment of cannabis use disorder
Bibliographic record
Abstract
Cannabis use has been continuously increasing, and cannabis use disorder (CUD) has become a public health issue. Some psychosocial interventions have demonstrated the ability to reduce cannabis use; however, there are no pharmacotherapies approved for the treatment of CUD. Some drugs have shown limited positive effects on use and withdrawal symptoms, but no controlled studies have been able to show strong and persistent effects on clinically meaningful outcomes. The aim of this review is to synthesize the evidence from the available literature regarding the effectiveness of psychosocial and pharmacological treatments for CUD among adults (that is, 18 years old or older). An analysis of the evidence shows that the current best psychosocial intervention to reduce cannabis use is the combination of motivational enhancement therapy and cognitive-behavioral therapy, preferably accompanied by a contingency management approach. In regard to pharmacological interventions, there are mostly unclear findings. Some drugs, such as CB1 agonists, gabapentin, and N-acetylcysteine, have been shown to produce improvements in some symptoms of CUD in single studies, but these have not been replicated. Other classes of medications, including antidepressants and antipsychotics, have been unsuccessful in producing such effects. There is an imminent need for more clinical trials to develop more effective treatments for CUD.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".