Comparative Effectiveness of Opioid Agonist Treatments: A Population-Based Study Protocol in Alberta
Bibliographic record
Abstract
The Comparative Effectiveness of Opioid Agonist Treatments: A Population-Based Study Protocol in Alberta is a retrospective observational study designed to evaluate and compare the real-world effectiveness of different opioid agonist therapies (OAT) for opioid use disorder (OUD) in Alberta. The study will analyze a provincial cohort of adults who initiated OAT—including buprenorphine/naloxone (sublingual and extended-release), methadone, and slow-release oral morphine (SROM) - between April 2022 and December 2024, using linked administrative health databases. The primary objective is to compare all-cause mortality and opioid overdose mortality among patients receiving these medications, both in the short term (within three months of treatment initiation) and long term (beyond three months). Secondary objectives include examining treatment retention, relapse rates, healthcare utilization, prescription patterns, and medication adherence. The study will use advanced statistical methods, including Cox proportional hazards models and propensity score weighting, to adjust for confounding factors and explore subgroup differences by age, sex, residence, and comorbidities. By providing robust comparative data on the outcomes of various OAT options, this research aims to inform clinical decision-making, policy development, and harm reduction strategies to address the opioid crisis in Alberta and improve health outcomes for individuals with OUD
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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 | 0.047 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".