Comparative effectiveness of buprenorphine-naloxone versus methadone for treatment of opioid use disorder: a population-based observational study protocol in British Columbia, Canada
Bibliographic record
Abstract
INTRODUCTION: Despite a recent meta-analysis including 31 randomised controlled trials comparing methadone and buprenorphine for the treatment of opioid use disorder, important knowledge gaps remain regarding the long-term effectiveness of different treatment modalities across individuals, including rigorously collected data on retention rates and other treatment outcomes. Evidence from real-world data represents a valuable opportunity to improve personalised treatment and patient-centred guidelines for vulnerable populations and inform strategies to reduce opioid-related mortality. Our objective is to determine the comparative effectiveness of methadone versus buprenorphine/naloxone, both overall and within key populations, in a setting where both medications are simultaneously available in office-based practices and specialised clinics. METHODS AND ANALYSIS: We propose a retrospective cohort study of all adults living in British Columbia receiving opioid agonist treatment (OAT) with methadone or buprenorphine/naloxone between 1 January 2008 and 30 September 2018. The study will draw on seven linked population-level administrative databases. The primary outcomes include retention in OAT and all-cause mortality. We will determine the effectiveness of buprenorphine/naloxone vs methadone using intention-to-treat and per-protocol analyses-the former emulating flexible-dose trials and the latter focusing on the comparison of the two medication regimens offered at the optimal dose. Sensitivity analyses will be used to assess the robustness of results to heterogeneity in the patient population and threats to internal validity. ETHICS AND DISSEMINATION: The protocol, cohort creation and analysis plan have been approved and classified as a quality improvement initiative exempt from ethical review (Providence Health Care Research Institute and the Simon Fraser University Office of Research Ethics). Dissemination is planned via conferences and publications, and through direct engagement and collaboration with entities that issue clinical guidelines, such as professional medical societies and public health organisations.
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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 | 0.038 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".