Effectiveness of slow-release oral morphine versus other OAT regimens in key sub-populations: protocol for population-based target trial emulation
Bibliographic record
Abstract
Introduction: Slow-release oral morphine (SROM) was introduced as an alternative form of opioid agonist treatment (OAT) in British Columbia (BC), Canada in 2017. While clinical guidelines in BC recommend SROM based on expert consensus and experience, there is limited real-world evidence on populations most likely to benefit from SROM compared to other forms of OAT, particularly in the context of widespread fentanyl exposure. We will estimate the comparative effectiveness of SROM versus methadone and buprenorphine/naloxone on OAT discontinuation and all-cause mortality among key sub-populations in BC. Methods: We will conduct a population-level retrospective cohort study using linked data from nine provincial health administrative databases. The study population includes adults (≥18 years) in BC who initiated SROM, methadone, or buprenorphine/naloxone between June 1, 2017, and December 31, 2022. Key sub-populations will include incident users with no prior OAT history and prevalent new users with prior OAT experience stratified by OAT type, stability, and medication switching history. The primary outcomes are time to OAT discontinuation and all-cause mortality, while overdose-related acute care visits will be examined as a secondary outcome. To estimate both the initiator effect and the effect of treatment at guideline-recommended doses, we will apply marginal structural models using inverse probability treatment weighting and 'clone-censor-weights' approach to address confounding by indication and time-varying confounding. Sensitivity analyses will evaluate the robustness of our findings, including cohort and timeline restrictions, alternative outcome definitions, and alternative estimation strategies including high-dimensional propensity score and instrumental variable approaches. Discussion: This study will generate real-world evidence on the comparative effectiveness of SROM versus methadone and buprenorphine/naloxone across clinically distinct OAT sub-populations. The findings will support evidence-informed updates to OAT guidelines and clinical decision-making in BC and other jurisdictions facing escalating opioid-related harms.
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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 | 0.066 | 0.100 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.105 | 0.024 |
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".