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Record W2325091407 · doi:10.4137/sart.s37030

A Prospective Study to Investigate Predictors of Relapse among Patients with Opioid Use Disorder Treated with Methadone

2016· article· en· W2325091407 on OpenAlexafffundabout
Leen Naji, Brittany B. Dennis, Monica Bawor, Carolyn Plater, Guillaume Paré, Andrew Worster, Michael Varenbut, Jeff Daiter, David C. Marsh, Dipika Desai, Lehana Thabane, Zainab Samaan

Bibliographic record

VenueSubstance Abuse Research and Treatment · 2016
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsHamilton Health SciencesSt. Joseph’s Healthcare HamiltonNOSM UniversityHamilton General HospitalPopulation Health Research InstituteCanadian Centre on Substance Use and AddictionMcMaster University
FundersMcMaster University
KeywordsOpioid use disorderMedicineHazard ratioOpioidMethadonePsychiatryProportional hazards modelAbstinenceProspective cohort studyMethadone maintenanceSubstance abuseInternal medicineAddictionComorbidityConfidence interval

Abstract

fetched live from OpenAlex

INTRODUCTION: Concomitant opioid abuse is a serious problem among patients receiving methadone maintenance treatment (MMT) for opioid use disorder. This is an exploratory study that aims to identify predictors of the length of time a patient receiving MMT for opioid use disorder remains abstinent (relapse-free). METHODS: Data were collected from 250 MMT patients enrolled in addiction treatment clinics across Southern Ontario. The impact of certain clinical and socio-demographic factors on the outcome (time until opioid relapse) was determined using a Cox proportional hazard model. RESULTS: History of injecting drug use behavior (hazard ratio (HR): 2.26, P = 0.042), illicit benzodiazepine consumption (HR: 1.07, P = 0.002), and the age of onset of opioid abuse (HR: 1.10, P < 0.0001) are important indicators of accelerated relapse among MMT patients. Conversely, current age is positively associated with duration of abstinence from illicit opioid use, serving as a protective factor against relapse (HR: 0.93, P = 0.003). CONCLUSION: This study helps to identify patients at increased risk of relapse during MMT, allowing health care providers to target more aggressive adjunct therapies toward high-risk patients.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.031
Threshold uncertainty score0.766

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.025
GPT teacher head0.291
Teacher spread0.266 · 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.

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

Citations51
Published2016
Admission routes3
Has abstractyes

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