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Record W4411379556 · doi:10.1136/bmjopen-2024-095645

Effectiveness of methadone versus buprenorphine in the treatment of opioid use disorder: secondary analyses of prospective cohort study data

2025· article· en· W4411379556 on OpenAlexafffundabout
Leen Naji, Tea Rosic, Brittany B. Dennis, Andrew Worster, James Paul, Lehana Thabane, Zainab Samaan

Bibliographic record

VenueBMJ Open · 2025
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsSt. Joseph’s Healthcare HamiltonUniversity of OttawaBritish Columbia Centre on Substance UseQueen's UniversityMcMaster UniversityImpact
FundersCanadian Institutes of Health Research
KeywordsMedicineBuprenorphineOpioid use disorderMethadoneOpiate Substitution TreatmentProspective cohort studyOpioidCohort studyPropensity score matchingCohort(+)-NaloxoneMethadone maintenanceInternal medicineEmergency medicinePsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVES: To compare the effectiveness of buprenorphine-naloxone (bup/nal) and methadone maintenance therapy (MMT) in the treatment of patients with opioid use disorder (OUD) during the fentanyl era. DESIGN: Secondary analysis of prospective cohort study data. SETTING: Data for the study were collected from 54 clinical sites across Ontario, Canada, between May 2018 and January 2023. PARTICIPANTS: To be included in the present study, participants had to be at least 16 years of age, have provided written informed consent and be receiving either MMT or bup/nal therapy for OUD. This study includes data from 2601 participants, of whom 2068 were receiving MMT and 533 were receiving bup/nal for OUD. The mean age of participants was 39.4 years (SD: 10.9), and 45% were female. INTERVENTIONS: MMT or bup/nal treatment for OUD. OUTCOME MEASURES: We employed a propensity score matched analysis to compare treatment outcomes among patients receiving MMT compared with bup/nal. We used ongoing illicit opioid use as an indicator of treatment outcome. We considered participants with >50% of urine drug screens in the past 12 months positive for non-prescribed opioids to be 'non-responders'. We conducted subgroup analyses to identify whether treatment type was associated with ongoing non-prescribed opioid use among patients with and without a history of intravenous drug use (IVDU), and whether treatment type was associated with retention in treatment. RESULTS: Eight per cent of patients on bup/nal were considered non-responders, compared with 11.9% of patients on MMT. We did not find a statistically significant association between treatment type and treatment response. However, we did find that patients on MMT were more likely to stay in treatment for 12 months (OR 1.79, 95% CI 1.45 to 2.22, p<0.001). We also found that, among patients without a history of IVDU, those on MMT were more likely to continue using non-prescribed opioids, compared with those on bup/nal (OR 1.72, 95% CI 1.07 to 2.77, p=0.023). CONCLUSIONS: Among a cohort of patients with OUD receiving treatment during the fentanyl era, we find that there is no statistically significant difference in ongoing non-prescribed opioid use between patients receiving MMT compared with bup/nal. Future studies should aim to further compare treatment effectiveness using patient-centred outcomes and pragmatic trial designs.

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)0.020
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.031
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.007
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.100
GPT teacher head0.451
Teacher spread0.352 · 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 designObservational
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

Citations3
Published2025
Admission routes3
Has abstractyes

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