Cannabis use and opioid relapse: An exploratory survival analysis of prospectively collected data
Bibliographic record
Abstract
Importance It is known that only minority of patients with opioid use disorder (OUD) receive treatment, of which only a fraction successfully complete treatment as intended. Factors associated with poor treatment outcomes remain unclear, and there is emerging but conflicting evidence that cannabis use may mitigate opioid use. Objective To analyze predictors of relapse amongst patients receiving buprenorphine-naloxone for OUD and identify the association between cannabis use and time to relapse. Design Data were prospectively collected between May 2018 and October 2020, and patients were followed for 12 months. Setting Thirty-one outpatient opioid agonist treatment clinics across Ontario, Canada. Participants All patients 16 years of age or older receiving buprenorphine-naloxone for OUD who had a urine toxicology screen negative for opioids at baseline were eligible for inclusion. Of the 488 patients consecutively sampled, 466 were included. Exposure Cannabis use. Main outcome and measure Relapse to opioid use assessed using urine toxicology screens. We employed a multivariable Cox-proportional hazard model for our analyses. Results We found that cannabis use was not protective against relapse [hazard ratio (HR) = 1.03, 95% confidence interval (CI): 0.78, 1.36, p = 0.84]. We found that participants who have been in treatment for at least two years had a 44% decrease in the hazard of relapse compared to those in treatment for less than a year (HR = 0.56, 95% CI: 0.34, 0.92, p = 0.021). We also found that the hazard of relapse was 2.6 times higher for participants who were intravenous drug users (HR = 2.61, 95% CI: 1.74, 3.91, p < 0.001), and that for every 1mg increase in the participants’ buprenorphine-naloxone dose, the hazard of relapse is 2% greater (HR = 1.02, 95% CI: 1.01, 1.03, p < 0.001). Conclusion Our analysis failed to show cannabis to be protective against relapse to opioid use in patients receiving buprenorphine-naloxone for OUD. We identified that individuals who inject drugs, are on higher doses of buprenorphine-naloxone, or have been in treatment for less than two years have a higher hazard for relapse. The presence of such factors may thus warrant closer patient follow-up and more stringent treatment protocols to mitigate risk of relapse and potential overdose.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".