The impact of methadone maintenance therapy on heptatis c incidence among illicit drug users
Bibliographic record
Abstract
Aims To determine the relationship between methadone maintenance therapy (MMT) and hepatitis C (HCV) seroconversion among illicit drug users. Design Generalized Estimating Equation model assuming a binomial distribution and a logit link function was used to examine for a possible protective effect of MMT use on HCV incidence. Setting Data from three prospective cohort studies of illicit drug users in Vancouver, Canada between 1996 and 2012. Participants 1004 HCV antibody negative illicit drug users stratified by exposure to MMT. Measurements Baseline and semi-annual HCV antibody testing and standardised interviewer administered questionnaire soliciting self-reported data relating to drug use patterns, risk behaviours, detailed sociodemographic data and status of active participation in an MMT program. Findings 184 HCV seroconversions were observed for an HCV incidence density of 6.32 [95% confidence interval [CI]: 5.44 – 7.31] per 100 person-years. After adjusting for potential confounders, MMT exposure was protective against HCV seroconversion (Adjusted Odds Ratio [AOR] = 0.47; 95% CI: 0.29 - 0.76). In sub-analyses, a dose-response protective effect of increasing MMT exposure on HCV incidence (AOR = 0.87; 95% CI: 0.78 – 0.97) per increasing 6-month period exposed to MMT was observed. Conclusion Participation in methadone maintenance treatment appears to be highly protective against hepatitis C incidence among illicit drug users. There appears to be a dose-response protective effect of increasing methadone exposure on hepatitis C incidence.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".