First-line opioid agonist treatment as prevention against assisting others in initiating injection drug use: A longitudinal cohort study of people who inject drugs in Vancouver, Canada
Why this work is in the frame
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Bibliographic record
Abstract
Background: Among people who inject drugs, frequent injecting and experiencing withdrawal are associated with facilitating others' first injections. As these factors may reflect an underlying substance use disorder, we investigated whether first-line oral opioid agonist treatment (OAT; methadone or buprenorphine/naloxone) reduces the likelihood that people who inject drugs help others initiate injecting. Methods: We used questionnaire data from semi-annual visits between December 2014-May 2018 on 334 people who inject drugs with frequent non-medical opioid use in Vancouver, Canada. We estimated the effect of current first-line OAT on subsequent injection initiation assistance provision (i.e., helped someone initiate injecting in the following six months) using inverse-probability-weighted estimation of repeated measures marginal structural models to reduce confounding and informative censoring by time-fixed and time-varying covariates. Results: By follow-up visit, 54-64% of participants reported current first-line OAT whereas 3.4-6.9% provided subsequent injection initiation assistance. Per the primary weighted estimate (n = 1114 person-visits), participants currently on first-line OAT (versus no OAT) were 50% less likely, on average, to subsequently help someone initiate injecting (relative risk [RR]=0.50, 95% CI=0.23-1.11). First-line OAT was associated with reduced risk of subsequent injection initiation assistance provision in participants who, at baseline, injected opioids less than daily (RR=0.15, 95% CI=0.05-0.44) but not in those who injected opioids daily (RR=0.86, 95% CI=0.35-2.11). Conclusions: First-line OAT seemingly reduces the short-term likelihood that people who inject drugs facilitate first injections. However, the extent of this potential effect remains uncertain due to imprecise estimation and observed heterogeneity by baseline opioid injecting frequency.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it