An on-site community-based model for hepatitis C screening, diagnosis, and treatment among people who inject drugs in Kerman, Iran: The Rostam study
Bibliographic record
Abstract
BACKGROUND: People who inject drugs (PWID) are at high risk for hepatitis C virus (HCV) infection and its complications in many countries, including Iran. This pilot study aimed to evaluate the effect of a community-based HCV model of care on HCV testing and treatment initiation among PWID in Kerman, Iran. METHODS: This study is part of the Rostam study and is a non-randomized trial evaluating the effect of on-site HCV- antibody rapid testing, venipuncture for HCV RNA testing, and treatment eligibility assessment on HCV testing and treatment initiation among PWID. Recruitment, interviews, and HCV screening, diagnosis, and treatment were all conducted at a community-based drop-in center (DIC) serving PWID clients. RESULTS: A total of 171 PWID (median age of 39 years and 89.5% male) were recruited between July 2018 and May 2019. Of 62 individuals who were HCV antibody positive, 47 (75.8%) were HCV RNA positive. Of RNA-positive individuals, 36 (76.6%) returned for treatment eligibility assessment. Of all the 36 participants eligible for treatment, 34 (94.4%) initiated HCV antiviral therapy. A sustained virologic response at 12 weeks post-treatment was 76.5% (26/34) in the intention-to-treat (ITT group) analysis and 100% (23/23) in the per-protocol (PP group) analysis. CONCLUSION: Our integrated on-site community-based HCV care model within a DIC setting suggested that HCV care including HCV testing and treatment uptake can be successfully delivered outside of hospitals or specialized clinics; a model which is more likely to reach PWID and can provide significant progress towards HCV elimination among this population.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".