Sativex Associated With Behavioral-Relapse Prevention Strategy as Treatment for Cannabis Dependence
Bibliographic record
Abstract
OBJECTIVES: Cannabis is the most commonly used illicit drug; a substantial minority of users develop dependence. The current lack of pharmacological treatments for cannabis dependence warrants the use of novel approaches and further investigation of promising pharmacotherapy. In this case series, we assessed the use of self-titrated dosages of Sativex (1:1, Δ-tetrahydrocannabinol [THC]/cannabidiol [CBD] combination) and motivational enhancement therapy and cognitive behavioral therapy (MET/CBT) for the treatment of cannabis dependence among 5 treatment-seeking community-recruited cannabis-dependent subjects. METHODS: Participants underwent a 3-month open-label self-titration phase with Sativex (up to 113.4 of THC/105 mg of CBD) and weekly MET/CBT, with a 3-month follow-up. RESULTS: Sativex was well-tolerated by all participants (average dosage 77.5 THC/71.7 mg CBD). The combination of Sativex and MET/CBT reduced the amount of cannabis use and progressively reduced craving and withdrawal scores. THC/CBD metabolite concentration indicated reduced cannabis use and compliance with medication. CONCLUSIONS: In summary, this pilot study found that with Sativex in combination with MET/CBT reduced cannabis use while preventing increases in craving and withdrawal in the 4 participants completing the study. Further systematic exploration of Sativex as a pharmacological treatment option for cannabis dependence should be performed.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".