Comparative effectiveness of methadone versus buprenorphine/naloxone during pregnancy on perinatal and neonatal health outcomes: protocol for a population-based target trial in British Columbia, Canada
Bibliographic record
Abstract
INTRODUCTION: Opioid use disorder (OUD) during pregnancy is associated with increased rates of adverse perinatal, foetal and neonatal health events. Opioid agonist treatment (OAT) can substantially reduce the risk of these potential harms. In British Columbia (BC), methadone and buprenorphine/naloxone are first-line treatment options for pregnant people with OUD. However, the comparative effectiveness of these regimens during pregnancy remains poorly understood, particularly in terms of how dosage may impact clinical outcomes. This protocol outlines a proposed population-based retrospective study to evaluate the comparative effectiveness of methadone compared with buprenorphine/naloxone during pregnancy on perinatal and neonatal health outcomes. METHODS AND ANALYSIS: We propose to conduct a retrospective observational study using population-based data from individuals who received methadone or buprenorphine/naloxone during pregnancy between 1 April 2010 and 31 March 2022. Data will be collected from 10 linked population-level administrative databases. We will emulate target trials using intention-to-treat and per-protocol approaches. We will use a pooled logistic regression approach to assess the impact of methadone versus buprenorphine/naloxone on time to OAT episode discontinuation and a dose-response marginal structural model to evaluate neonatal health at delivery. An exploratory observational analysis will also be conducted to describe the impact of methadone vs buprenorphine exposure during the first trimester of pregnancy on congenital malformations and anomalies. ETHICS AND DISSEMINATION: This study has been determined to meet the criteria for exemption per Article 2.5 of the 2018 Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans. Study databases have been made available by the BC Ministries of Health and Mental Health and Addiction as part of the provincial opioid overdose public health emergency response. Results will be disseminated to policymakers, clinical partners, community programmes and people with lived and living experience of substance use and published in peer-reviewed journals.
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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 | 0.040 | 0.044 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".