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Record W2990026571 · doi:10.9778/cmajo.20190026

Cannabis use during methadone maintenance treatment for opioid use disorder: a systematic review and meta-analysis

2019· review· en· W2990026571 on OpenAlexaffvenue
Heather McBrien, Candice Luo, Nitika Sanger, Laura Zielinski, Meha Bhatt, Xi Ming Zhu, David C. Marsh, Lehana Thabane, Zainab Samaan

Bibliographic record

VenueCMAJ Open · 2019
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsMcMaster UniversityNOSM UniversitySt. Joseph’s Healthcare HamiltonMcMaster University Medical Centre
Fundersnot available
KeywordsCannabisMethadoneMethadone maintenanceMedicinePsycINFOOpioidOpioid use disorderPsychiatryObservational studyMeta-analysisMEDLINECINAHLInternal medicinePsychological intervention

Abstract

fetched live from OpenAlex

BACKGROUND: Rates of cannabis use among patients receiving methadone maintenance therapy are high, and cannabis use may be associated with outcomes of methadone maintenance therapy. We examined the effect of cannabis use on opioid use in patients receiving methadone maintenance therapy to test the hypothesis that cannabis use is associated with a reduction in opioid use. METHODS: In this systematic review, we searched MEDLINE/PubMed, Embase, PsycINFO, CINAHL and ProQuest Dissertations and Theses Global from inception to July 12, 2018. We summarized the effects of cannabis use on opioid use during methadone maintenance therapy and treatment retention. We conducted meta-analyses using a random effects model. RESULTS: We included 23 studies in our review. We performed a meta-analysis of 6 studies, with a total number of participants of 3676, examining use of cannabis and opioids during methadone maintenance therapy. Owing to high heterogeneity, we described the studies qualitatively but provide the forest plots as supplemental material. The overall quality of evidence was very low, with a high risk of bias, owing to the nature of observational studies. INTERPRETATION: CRD42015029372.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.808
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0180.005
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.180
GPT teacher head0.421
Teacher spread0.241 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations32
Published2019
Admission routes2
Has abstractyes

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