Comparative effectiveness of maintenance doses of opioid agonist treatment among individuals with opioid use disorder: a target trial emulation protocol using a population-based observational study
Bibliographic record
Abstract
INTRODUCTION: Opioid agonist treatment (OAT) prescribing patterns have shifted in recent years in British Columbia (BC), Canada due to the increasingly toxic unregulated drug supply. Experimental evidence to support guidelines on the effectiveness of maintaining clients at different maintenance dosage levels is incomplete and outdated for the fentanyl era. Our objective is to assess the risk of treatment discontinuation and mortality among individuals receiving different maintenance dosage strategies for OAT with methadone, buprenorphine/naloxone or slow-release oral morphine (SROM) at the population level in BC, Canada. METHODS AND ANALYSIS: We propose a retrospective population-level study of BC residents initiating OAT on methadone, buprenorphine/naloxone or SROM between 1 January 2010 and 31 December 2022 who were ≥18 years of age with no known pregnancy, no history of cancer diagnosis or receiving palliative care and not currently incarcerated. Our study will employ health administrative databases linked at the individual level to emulate a target trial per OAT type where individuals will be assigned to discrete maintenance dosing strategies, according to the full range observed in BC during the study period. Primary outcomes include treatment discontinuation and all-cause mortality. To determine the effectiveness of alternative maintenance doses, we will emulate a 'per-protocol' trial using a clone-censor-weight approach to adjust for measured time-dependent confounding by indication. ETHICS AND DISSEMINATION: The protocol, cohort creation and analysis plan have been classified and approved as a quality improvement initiative by Providence Health Care Research Ethics Board and the Simon Fraser University Office of Research Ethics. All data are deidentified, securely stored and accessed in accordance with provincial privacy regulations. Results will be disseminated and shared with local advocacy groups and decision-makers, developers of national and international clinical guidelines, presented at national and international conferences and published in peer-reviewed journals electronically and in print.
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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 | 0.201 | 0.205 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".