Cannabis use during methadone maintenance treatment for opioid use disorder: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Rates of cannabis use among patients receiving methadone maintenance therapy are high, and cannabis use may be associated with outcomes of methadone maintenance therapy. We examined the effect of cannabis use on opioid use in patients receiving methadone maintenance therapy to test the hypothesis that cannabis use is associated with a reduction in opioid use. METHODS: In this systematic review, we searched MEDLINE/PubMed, Embase, PsycINFO, CINAHL and ProQuest Dissertations and Theses Global from inception to July 12, 2018. We summarized the effects of cannabis use on opioid use during methadone maintenance therapy and treatment retention. We conducted meta-analyses using a random effects model. RESULTS: We included 23 studies in our review. We performed a meta-analysis of 6 studies, with a total number of participants of 3676, examining use of cannabis and opioids during methadone maintenance therapy. Owing to high heterogeneity, we described the studies qualitatively but provide the forest plots as supplemental material. The overall quality of evidence was very low, with a high risk of bias, owing to the nature of observational studies. INTERPRETATION: CRD42015029372.
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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.026 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".