Bibliographic record
Abstract
Background: People who inject drugs (PWID) are at high risk of hepatitis C (HCV) infection.Prescription opioid (PO) injection appears to increase HCV risk relative to heroin injection, but the impact of PO dosage release formulation (immediate vs. extended-release) remains unclear.Objective: To compare HCV incidence among PWID injecting different types of opioids in Montreal.Methods: Data were from the Montreal Hepatitis Cohort (HEPCO) of PWID (2011PWID ( -2020)).HCV RNA-negative participants completed interviewer-administered questionnaires and blood sampling every three months.Self-reported past-month opioid injection was categorized into no opioid injection, heroin injection (without PO injection), extended-release PO injection, and other PO injection.Multivariate time-dependent Cox regression (adjusting for biological sex, age, and calendar year) estimated adjusted Hazard Ratios (aHR) for time to new HCV infection.Results: Of 809 cohort participants, 580 HCV RNA-negative people (82% male, median age 40) contributed 5465 visits across 1861 person-years of follow-up.Overall, 134 acquired HCV for an incidence of 7.2 per 100 person-years.Crude HCV incidence (per 100 person-years) across categories was: no opioid injection 3.6 (95%CI 2.6-4.8),heroin injection 3.3 (95%CI 1.6-6.1),any extended-release opioid injection 16.3 (95%CI 4.1-44.3),and other prescription opioid injection 21.1 (95%CI 16.9-26.2).Compared with no opioid injection, risk of HCV infection was elevated among people injecting extended-release PO (aHR 3.5, 95% CI 1.1-11.2) or other PO (aHR 4.6, 95%CI 3.0-6.8)but not heroin (aHR 0.92, 95%CI 0.44-1.94).Conclusions: Among PWID in Montreal, HCV risk is elevated when injecting prescription opioids and differs little between immediate-and extended-release forms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.684 | 0.488 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".