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Record W4388014892 · doi:10.1177/07067437231210796

Associations Between Buprenorphine\Naloxone and Methadone Treatment and non-Opioid Substance Use in Prescription-Type Opioid Use Disorder: Secondary Analyses From the OPTIMA Study: Associations entre le traitement avec la buprénorphine/naloxone et avec la méthadone et l'utilisation de substances non opioïdes dans le trouble lié à l'usage d'opioïdes de type sur ordonnance : analyses secondaires de l'étude OPTIMA

2023· article· en· W4388014892 on OpenAlexafffundvenueabout
Hamzah Bakouni, Heidar Sharafi, Sarah Drouin, Raphaëlle Fortin, Stéphanie Marsan, Suzanne Brissette, M. Eugenia Socías, Bernard Le Foll, Ron Lim, Didier Jutras‐Aswad

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2023
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsWaypoint Centre for Mental Health CareUniversity of TorontoCentre for Addiction and Mental HealthBritish Columbia Centre on Substance UseUniversité de MontréalPublic Health OntarioUniversity of British ColumbiaUniversity of CalgaryCentre Hospitalier de l’Université de Montréal
FundersCanadian Institutes of Health ResearchHealth CanadaUniversity of TorontoMichael Smith Health Research BC
KeywordsBuprenorphineMethadoneOpioid use disorderOpioid(+)-NaloxoneMedicineMedical prescriptionPsychiatryPharmacologyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: There is limited evidence on how opioid agonist treatment (OAT) may affect psychoactive non-opioid substance use in prescription-type opioid use disorder (POUD) and whether this effect might explain OAT outcomes. We aimed to assess the effect of methadone on non-opioid substance use compared to buprenorphine/naloxone (BUP/NX), to explore whether non-opioid substance use is associated with opioid use and retention in treatment, and to test non-opioid use as a moderator of associations between methadone with retention in OAT and opioid use compared to BUP/NX. METHODS: This is a secondary analysis of data from the OPTIMA trial, an open-label, pragmatic, parallel, two-arm, pan-Canadian, multicentre, randomized-controlled trial to compare standard methadone model of care and flexible take-home dosing BUP/NX for POUD treatment. We studied the effect of methadone and BUP/NX on non-opioid substance use evaluated by urine drug screen (UDS) and by classes of non-opioid substances (i.e., tetrahydrocannabinol [THC], benzodiazepines, stimulants) (weeks 2-24) using adjusted generalized estimation equation (GEE). We studied the association between non-opioid substance-positive UDS and opioid-positive UDS and retention in treatment, using adjusted GEE and logistic regressions. RESULTS: Overall, methadone was not associated with non-opioid substance-positive UDS compared to BUP/NX (OR: 0.78; 95%CI, 0.41 to 1.48). When non-opioid substances were studied separately, methadone was associated with lower odds of benzodiazepine-positive UDS (OR: 0.63; 95% CI: 0.40 to 0.98) and THC-positive UDS (OR: 0.47; 95% CI: 0.28 to 0.77), but not with different odds of stimulant-positive UDS (OR: 1.29; 95% CI: 0.78 to 2.16) compared to BUP/NX. Substance-positive UDS, overall and separate classes, were not associated with opioid-positive UDS or retention in treatment. CONCLUSION: Methadone did not show a significant effect on overall non-opioid substance use in POUD compared to BUP/NX treatment but was associated with lower odds of benzodiazepine and THC use in particular. Non-opioid substance use did not predict OAT outcomes. Further research is needed to ascertain whether specific patterns of polysubstance use (quantity and frequency) may affect treatment outcomes.

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)0.007
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.006
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.045
GPT teacher head0.315
Teacher spread0.270 · 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

Citations1
Published2023
Admission routes4
Has abstractyes

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