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Record W4403485032 · doi:10.1001/jama.2024.16954

Buprenorphine/Naloxone vs Methadone for the Treatment of Opioid Use Disorder

2024· article· en· W4403485032 on OpenAlexaffabout
Bohdan Nosyk, Jeong Eun Min, Fahmida Homayra, Megan Kurz, Brenda Carolina Guerra‐Alejos, Ruyu Yan, Micah Piske, Shaun R. Seaman, Paxton Bach, Sander Greenland, Mohammad Ehsanul Karim, Uwe Siebert, Julie Bruneau, Paul Gustafson, Kyle M. Kampman, P. Todd Korthuis, Thomas M. Loughin, Lawrence C. McCandless, Robert W. Platt, Kevin Schnepel, M. Eugenia Socías

Bibliographic record

VenueJAMA · 2024
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsUniversité de MontréalBritish Columbia Centre on Substance UseMcGill UniversityCentre Hospitalier de l’Université de MontréalUniversity of British ColumbiaCentre for Advancing Health OutcomesSimon Fraser University
FundersNational Institute on Drug Abuse
KeywordsMedicineBuprenorphineMethadoneDiscontinuationOpioid use disorder(+)-NaloxoneHazard ratioOpioidRetrospective cohort studyPopulationConfidence intervalDosingCohort studyEmergency medicineAnesthesiaInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Importance: Previous studies on the comparative effectiveness between buprenorphine and methadone provided limited evidence on differences in treatment effects across key subgroups and were drawn from populations who use primarily heroin or prescription opioids, although fentanyl use is increasing across North America. Objective: To assess the risk of treatment discontinuation and mortality among individuals receiving buprenorphine/naloxone vs methadone for the treatment of opioid use disorder. Design, Setting, and Participants: Population-based retrospective cohort study using linked health administrative databases in British Columbia, Canada. The study included treatment recipients between January 1, 2010, and March 17, 2020, who were 18 years or older and not incarcerated, pregnant, or receiving palliative cancer care at initiation. Exposures: Receipt of buprenorphine/naloxone or methadone among incident (first-time) users and prevalent new users (including first and subsequent treatment attempts). Main Outcomes and Measures: Hazard ratios (HRs) with 95% compatibility (confidence) intervals were estimated for treatment discontinuation (lasting ≥5 days for methadone and ≥6 days for buprenorphine/naloxone) and all-cause mortality within 24 months using discrete-time survival models for comparisons of medications as assigned at initiation regardless of treatment adherence ("initiator") and received according to dosing guidelines (approximating per-protocol analysis). Results: A total of 30 891 incident users (39% receiving buprenorphine/naloxone; 66% male; median age, 33 [25th-75th, 26-43] years) were included in the initiator analysis and 25 614 in the per-protocol analysis. Incident users of buprenorphine/naloxone had a higher risk of treatment discontinuation compared with methadone in initiator analyses (88.8% vs 81.5% discontinued at 24 months; adjusted HR, 1.58 [95% CI, 1.53-1.63]), with limited change in estimates when evaluated at optimal dose in per-protocol analysis (42.1% vs 30.7%; adjusted HR, 1.67 [95% CI, 1.58-1.76]). Per-protocol analyses of mortality while receiving treatment exhibited ambiguous results among incident users (0.08% vs 0.13% mortality at 24 months; adjusted HR, 0.57 [95% CI, 0.24-1.35]) and among prevalent users (0.08% vs 0.09%; adjusted HR, 0.97 [95% CI, 0.54-1.73]). Results were consistent after the introduction of fentanyl and across patient subgroups and sensitivity analyses. Conclusions and Relevance: Receipt of methadone was associated with a lower risk of treatment discontinuation compared with buprenorphine/naloxone. The risk of mortality while receiving treatment was similar for buprenorphine/naloxone and methadone, although the CI estimate for the hazard ratio was wide.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.025
GPT teacher head0.299
Teacher spread0.274 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations54
Published2024
Admission routes2
Has abstractyes

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