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Record W6906586262 · doi:10.17605/osf.io/5zty6

Comparative Effectiveness of Opioid Agonist Treatments: A Population-Based Study Protocol in Alberta

2025· other· en· W6906586262 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Science Framework · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsPropensity score matchingOpioidObservational studyConfoundingRetrospective cohort studyMedical prescriptionProportional hazards modelCohort studyProtocol (science)

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.359
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.006
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0040.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.031
GPT teacher head0.423
Teacher spread0.392 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreProtocol

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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