The Cannabis-Dependent Relationship Between Methadone Treatment Dose and Illicit Opioid Use in a Community-Based Cohort of People Who Use Drugs
Bibliographic record
Abstract
Background: Methadone maintenance treatment (MMT) is an effective treatment for opioid use disorder. However, subtherapeutic dosing may lead to continued opioid use by failing to suppress opioid withdrawal and craving. Preclinical and pilot experimental research suggests that cannabinoids may reduce opioid withdrawal and craving. We sought to test whether the association between low methadone dose and illicit opioid use differs according to concurrent cannabis use patterns. Methods: Data for this study were derived from two community-recruited cohorts of people (≥18 years old) who use illicit drugs in Vancouver, Canada. We used generalized estimating equations to estimate the adjusted association between lower daily MMT dose (<90 mg/day) and daily illicit opioid use, testing for interaction between dose and daily cannabis use. Results: Between December 2005 and December 2018, 1389 participants reported MMT enrolment and were included in the study. We observed a significant interaction ( p <0.01) between daily cannabis and lower MMT dose on concurrent daily illicit opioid use: lower MMT doses increased the odds of daily illicit opioid use by 86% (adjusted odds ratio [AOR]=1.86, 95% confidence interval [CI]=1.61–2.16) during periods of no or low-frequency cannabis use and by 30% during periods of daily cannabis use (AOR=1.30, 95% CI=1.01–1.67). Discussion: This study provides preliminary observational evidence that cannabis may mitigate some of the negative effects of subtherapeutic MMT dosing, guiding future clinical investigations into the safety and efficacy of cannabis and cannabinoids as adjunct treatment for MMT.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".