Effectiveness of methadone versus buprenorphine in the treatment of opioid use disorder: secondary analyses of prospective cohort study data
Bibliographic record
Abstract
OBJECTIVES: To compare the effectiveness of buprenorphine-naloxone (bup/nal) and methadone maintenance therapy (MMT) in the treatment of patients with opioid use disorder (OUD) during the fentanyl era. DESIGN: Secondary analysis of prospective cohort study data. SETTING: Data for the study were collected from 54 clinical sites across Ontario, Canada, between May 2018 and January 2023. PARTICIPANTS: To be included in the present study, participants had to be at least 16 years of age, have provided written informed consent and be receiving either MMT or bup/nal therapy for OUD. This study includes data from 2601 participants, of whom 2068 were receiving MMT and 533 were receiving bup/nal for OUD. The mean age of participants was 39.4 years (SD: 10.9), and 45% were female. INTERVENTIONS: MMT or bup/nal treatment for OUD. OUTCOME MEASURES: We employed a propensity score matched analysis to compare treatment outcomes among patients receiving MMT compared with bup/nal. We used ongoing illicit opioid use as an indicator of treatment outcome. We considered participants with >50% of urine drug screens in the past 12 months positive for non-prescribed opioids to be 'non-responders'. We conducted subgroup analyses to identify whether treatment type was associated with ongoing non-prescribed opioid use among patients with and without a history of intravenous drug use (IVDU), and whether treatment type was associated with retention in treatment. RESULTS: Eight per cent of patients on bup/nal were considered non-responders, compared with 11.9% of patients on MMT. We did not find a statistically significant association between treatment type and treatment response. However, we did find that patients on MMT were more likely to stay in treatment for 12 months (OR 1.79, 95% CI 1.45 to 2.22, p<0.001). We also found that, among patients without a history of IVDU, those on MMT were more likely to continue using non-prescribed opioids, compared with those on bup/nal (OR 1.72, 95% CI 1.07 to 2.77, p=0.023). CONCLUSIONS: Among a cohort of patients with OUD receiving treatment during the fentanyl era, we find that there is no statistically significant difference in ongoing non-prescribed opioid use between patients receiving MMT compared with bup/nal. Future studies should aim to further compare treatment effectiveness using patient-centred outcomes and pragmatic trial designs.
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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 | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".