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Opioid Coprescription Through Risk Mitigation Guidance and Opioid Agonist Treatment Receipt

2024· article· en· W4396921336 on OpenAlexaffabout
Jeong Eun Min, Brenda Carolina Guerra‐Alejos, Ruyu Yan, Heather Palis, Brittany Barker, Karen Urbanoski, Bernie Pauly, Amanda Slaunwhite, Paxton Bach, Corey Ranger, Ashley Heaslip, Bohdan Nosyk

Bibliographic record

VenueJAMA Network Open · 2024
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsBritish Columbia Centre on Substance UseIsland HealthBC Centre for Disease ControlUniversity of British ColumbiaUniversity of VictoriaSimon Fraser UniversityCentre for Advancing Health Outcomes
FundersNational Institute on Drug Abuse
KeywordsOpioidReceiptMedicineAgonistAnesthesiaIntensive care medicineBusinessInternal medicineReceptor

Abstract

fetched live from OpenAlex

Importance: At the onset of the COVID-19 pandemic, the government of British Columbia, Canada, released clinical guidance to support physicians and nurse practitioners in prescribing pharmaceutical alternatives to the toxic drug supply. These alternatives included opioids and other medications under the risk mitigation guidance (RMG), a limited form of prescribed safer supply, designed to reduce the risk of SARS-CoV-2 infection and harms associated with illicit drug use. Many clinicians chose to coprescribe opioid medications under RMG alongside opioid agonist treatment (OAT). Objective: To examine whether prescription of hydromorphone tablets or sustained-release oral morphine (opioid RMG) and OAT coprescription compared with OAT alone is associated with subsequent OAT receipt. Design, Setting, and Participants: This population-based, retrospective cohort study was conducted from March 27, 2020, to August 31, 2021, included individuals from 10 linked health administrative databases from British Columbia, Canada. Individuals who were receiving OAT at opioid RMG initiation and individuals who were receiving OAT and eligible but unexposed to opioid RMG were propensity score matched at opioid RMG initiation on sociodemographic and clinical variables. Data were analyzed between January 2023 and February 2024. Exposure: Opioid RMG receipt (≥4 days, 1-3 days, or 0 days of opioid RMG dispensed) in a given week. Main Outcome and Measures: The main outcome was OAT receipt, defined as at least 1 dispensed dose of OAT in the subsequent week. A marginal structural modeling approach was used to control for potential time-varying confounding. Results: A total of 4636 individuals (2955 [64%] male; median age, 38 [31-47] years after matching) were receiving OAT at the time of first opioid RMG dispensation (2281 receiving ongoing OAT and 2352 initiating RMG and OAT concurrently). Opioid RMG receipt of 1 to 3 days in a given week increased the probability of OAT receipt by 27% in the subsequent week (adjusted risk ratio, 1.27; 95% CI, 1.25-1.30), whereas receipt of opioid RMG for 4 days or more resulted in a 46% increase in the probability of OAT receipt in the subsequent week (adjusted risk ratio, 1.46; 95% CI, 1.43-1.49) compared with those not receiving opioid RMG. The biological gradient was robust to different exposure classifications, and the association was stronger among those initiating opioid RMG and OAT concurrently. Conclusions and Relevance: This cohort study, which acknowledged the intermittent use of both medications, demonstrated that individuals who were coprescribed opioid RMG had higher adjusted probability of continued OAT receipt or reengagement compared with those not receiving opioid RMG.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.492
Threshold uncertainty score0.978

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.304
Teacher spread0.283 · 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

Citations13
Published2024
Admission routes2
Has abstractyes

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