Methadone Dose, Cannabis Use, and Treatment Retention: Findings From a Community-based Sample of People Who Use Unregulated Drugs
Bibliographic record
Abstract
OBJECTIVES: Lower daily methadone dose is negatively associated with retention in methadone maintenance treatment (MMT). Cannabis use during MMT is common, with many patients reporting its use for opioid withdrawal mitigation. We sought to test whether the association between lower MMT dose and treatment retention differs by concurrent high-frequency cannabis use in a community sample of people on MMT. METHODS: We obtained data from participants initiating MMT in 2 community-recruited prospective cohorts of people who use drugs in Vancouver, Canada. We built multivariable Cox frailty models to estimate the relationships between MMT dose (<90 mg/d vs ≥90 mg/d) and time to treatment discontinuation. We included an interaction term to test whether high-frequency (≥daily) cannabis use modified the measured effect of lower treatment dose on treatment retention. RESULTS: Between December 2005 and December 2018, 829 participants (54.1%) initiated at least 1 MMT episode and were included in the analysis. Lower MMT dose was strongly positively associated with treatment discontinuation regardless of concurrent high-frequency cannabis use (interaction P > 0.05). Structural factors including homelessness and incarceration were significantly and positively associated with treatment discontinuation. CONCLUSIONS: Although we previously found the magnitude and strength of the relationship between lower MMT dose and high-frequency unregulated opioid use to be tempered during high-frequency cannabis use periods, this effect measure modification does not appear to translate to time retained in treatment. Cannabis-based interventions to promote retention in MMT are unlikely to produce long-term benefit without addressing external factors that place MMT patients at increased risk of treatment discontinuation.
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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 | 0.000 |
| 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".