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Record W7132930916

Assessing the Potential of Oral Opioid Agonist Treatment to Prevent Injection Drug Use Initiation Assistance Provision in People Who Inject Drugs

2023· dissertation· W7132930916 on OpenAlexaboutno aff
Zachary Bouck

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsnot available
Fundersnot available
KeywordsOpioidMethadoneMedical prescriptionCohortConfidence intervalOpioid use disorderDrugMorphine
DOInot available

Abstract

fetched live from OpenAlex

Injection drug use is a significant risk factor for morbidity and mortality from overdose and injection-related infections. Most first-time injections involve assistance from experienced people who inject drugs (PWID), with frequent injecting and withdrawal associated with assistance provision. Initial cross-sectional data suggests that first-line oral opioid agonist treatment (OAT) with methadone or buprenorphine/naloxone for opioid use disorder may reduce the likelihood that PWID facilitate first injections. This thesis evaluates whether available oral OAT medications in Canada can prevent injection initiation assistance provision in PWID. I first validated common questionnaire-based measures of current and recent first-line OAT enrollment in a sample of PWID in Toronto, relative to prescription dispensation claims data. Both self-reported measures were relatively accurate, though participants tended to underreport recent treatment and overreport current treatment. Using marginal structural models, I then analyzed questionnaire data from December 2014-May 2018 to assess whether current first-line OAT reduces the short-term risk of providing injection initiation assistance in a cohort of Vancouver-based PWID with frequent non-medical opioid use. Participants on first-line OAT (versus no OAT) were 50% less likely, on average, to help someone initiate injecting in the following six months. However, the corresponding 95% confidence interval (0.23-1.11) was imprecise due to small sample size, outcome rarity, and a weaker effect in people with daily baseline opioid injecting. Adjusting for self-reported treatment misclassification yielded comparable results. Lastly, I conducted a prevalent new-user cohort study using questionnaire data from June 2017-March 2020 on Vancouver-based PWID to evaluate the effectiveness of OAT with slow-release oral morphine (SROM) on non-medical opioid injecting and injection initiation assistance provision versus first-line OAT. After matching to reduce confounding, recent SROM initiation was not associated with a differential likelihood of daily non-medical opioid injecting or providing injection initiation assistance over the same six-month period in new users. Collectively, these findings provide additional support for oral OAT as prevention against injection initiation assistance provision in PWID, while reinforcing a need for alternative medications for those who have not adequately benefitted from previous oral OAT. Further research could explore the impact of higher-intensity injectable OAT on injection initiation assistance provision.

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)0.009
metaresearch head score (Gemma)0.053
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 score0.362
Threshold uncertainty score0.720

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.053
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.002
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 head0.042
GPT teacher head0.405
Teacher spread0.363 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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