Real‐world hepatitis C prevalence and treatment uptake at opioid agonist therapy clinics in Ontario, Canada
Bibliographic record
Abstract
Widespread screening for hepatitis C virus (HCV) is necessary for Canada to meet its HCV elimination goals by 2030. People who currently or previously injected drugs are at high risk for HCV. Opioid agonist therapy (OAT, such as methadone and buprenorphine) has been shown to help stabilize the lives of people who are opioid-dependent. The distribution of OAT in North America typically requires daily, weekly, or monthly clinic visits and presents an opportunity for engagement, screening and treatment for those at high-risk of HCV. In this study, HCV screening was conducted by staff at OAT clinics in Ontario from 2016 to 2020 and those with chronic infections were treated on-site with direct-acting antivirals. Point-of-care or dried blood spot (DBS) testing was used for antibodies, DBS or serum for HCV RNA and serum for HCV RNA at SVR12 (sustained virological response). Clinics screened 1954 people (mean age 40 years ±12, 63% male). Forty-five percent were antibody positive, of whom 64% were HCV RNA+. Eighty percent of those RNA+ set an appointment in which 99% attended. Ninety-six percent started treatment with 87% completing treatment. Sixty-eight percent of people who completed treatment submitted a sample for SVR12 testing of which 97% achieved a virological cure. Results suggest that HCV screening and treatment at OAT clinics is feasible, effective and warrants expansion. Data suggest strong treatment adherence due to high rates of SVR12 comparable with other OAT-based HCV treatment programs. The lack of SVR12 sampling could be addressed by either on-site phlebotomy or incentivizing SVR12 sampling.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".