Factors associated with <scp>HCV</scp> antiviral treatment uptake among participants of a community‐based <scp>HCV</scp> programme for marginalized patients
Bibliographic record
Abstract
While the majority of cases of hepatitis C virus (HCV) in developed countries occur among illicit drug users, HCV antiviral treatment uptake is poor in this population. Several studies have shown that patients can successfully be treated for HCV in the context of methadone maintenance programmes, but little evidence exists evaluating HCV treatment models for substance users where methadone maintenance is not indicated. This retrospective cohort study involved 129 persons participating in psycho-educational support groups and integrated, interprofessional, community-based health services focused on the treatment for HCV among marginalized populations with high rates of crack cocaine use and mental health comorbidities. We sought to identify the factors associated with antiviral treatment uptake. Group participation improved access to health care. While 19% had previously seen an HCV specialist prior to group initiation, 59% saw an HCV specialist during the group. Half of the participants were nonimmune to hepatitis A or B at baseline, and 80% of these patients received immunization through the programme. The programme treated 24 patients with pegylated interferon and ribavirin and achieved a sustained virologic response (SVR) rate of 91% for genotype 2 or 3 and 54% for genotype 1. Stable housing was independently associated with initiation of treatment, and there was a nonsignificant trend towards lower rates of treatment initiation among women. SVR rates for those who had used crack or injection drugs in the month prior to joining the programme did not differ significantly from those who had abstained.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".