Risk of opioid-related mortality associated with buprenorphine versus methadone: A systematic review of observational studies
Bibliographic record
Abstract
ABSTRACT Introduction Buprenorphine and methadone are effective treatments of opioid use disorder (OUD) and can reduce drug-related mortality. While observational studies have compared head-to-head buprenorphine and methadone, this evidence has not been previously synthesized. Our study aims to systematically review the available evidence on the comparative effectiveness of buprenorphine and methadone in people with OUD, thereby rigorously assessing the methodological quality of individual studies. Methods We searched Medline, Embase, PsycINFO, and Web of Science for all relevant articles published between 1978 and April 8, 2023. Observational studies directly comparing the risk of drug-related mortality between buprenorphine and methadone among people with OUD were eligible. We assessed the overall risk of bias using the Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I) tool. Results Our systematic review included seven studies. There was mixed evidence of comparative mortality risk, with heterogeneity across study region, time, and treatment status (on treatment vs. discontinued). Three studies reported no difference, and four reported findings in favour of buprenorphine. Based on ROBINS-I, three studies had a moderate risk of bias, two had a severe risk, and two had a critical risk. Major sources of biases were residual confounding and selection bias along with presence of prevalent user bias, informative censoring, and left truncation. Conclusions Due to methodological limitations of the observational studies, generalizability of their findings remains unknown. Therefore, to provide a more accurate comparative safety profile for these two medications, further observational studies with methodological rigour are warranted.
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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 | 0.033 | 0.143 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".