Cannabis use and illicit opioid cessation among people who use drugs living with chronic pain
Bibliographic record
Abstract
INTRODUCTION: Amidst the opioid overdose crisis, there is interest in cannabis use for pain management and harm reduction. We investigated the relationship between cannabis use and cessation of unregulated opioid use among people who use drugs (PWUD) living with chronic pain. METHOD: Data for analyses were collected from three prospective cohort studies in Vancouver, Canada. All cohort participants who completed at least two study visits and reported both pain and unregulated opioid use in the past 6 months were included in the present study. We analysed the association between cannabis use frequency and opioid cessation rates using extended Cox regression models with time-updated covariates. RESULTS: Between June 2014 and May 2022, 2340 PWUD were initially recruited and of those 1242 PWUD reported chronic pain, use of unregulated opioids and completed at least two follow-up visits. Of these 1242 participants, 764 experienced a cessation event over 1038.2 person-years resulting in a cessation rate of 28.5 per 100 person-years (95% confidence interval [CI] 25.4-31.9). Daily cannabis use was positively associated with opioid cessation (adjusted hazard ratio 1.40, 95% CI 1.08-1.81; p = 0.011). In the sex-stratified sub-analyses, daily cannabis use was significantly associated with increased rates of opioid cessation among males (adjusted hazard ratio 1.50, 95% CI 1.09-2.08; p = 0.014). DISCUSSION AND CONCLUSIONS: Participants reporting daily cannabis use exhibited higher rates of cessation compared to less frequent users or non-users. Observed sex-specific differences in cannabis use and opioid cessation suggest potential differences in cannabis use behaviours and effects. Our findings add to the growing evidence supporting the potential benefits of cannabis use among PWUD, underlining the need for further research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".