Clinical and Patient-Reported Outcomes of Direct-Acting Antivirals for the Treatment of Chronic Hepatitis C Among Patients on Opioid Agonist Treatment: A Real-world Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Patient-reported outcomes (PROs) can help to reduce uncertainties about hepatitis C virus (HCV) treatment with direct-acting antivirals (DAAs) among people who inject drugs and increase treatment uptake in this high-risk group. Besides clinical data, this study analyzed for the first time PROs in a real-world sample of patients on opioid agonist treatment (OAT) and HCV treatment with DAAs. METHODS: HCV treatment data including virological response, adherence, safety, and PROs of 328 German patients on OAT were analyzed in a pragmatic prospective cohort study conducted from 2016 to 2018. Clinical effectiveness was defined as sustained virological response (SVR) at week 12 after end of treatment and calculated in per-protocol (PP) and intention-to-treat (ITT) analyses. Changes over time in PROs on health-related quality of life, physical and mental health, functioning, medication tolerability, fatigue, concentration, and memory were analyzed by repeated-measures analyses of variances (ANOVAs). RESULTS: We found high adherence and treatment completion rates, a low number of mainly mild adverse events, and high SVR rates (PP: 97.5% [n = 285]; ITT: 84.5% [n = 328]). Missing SVR data in the ITT sample were mainly caused by patients lost to follow-up after treatment completion. Most PROs showed statistically significant but modest improvements over time, with more pronounced improvements in highly impaired patients. CONCLUSIONS: This real-world study confirms that DAA treatment among OAT patients is feasible, safe, and effective. PROs show that all patients, but particularly those with higher somatic, mental, and social burden, benefit from DAA treatment.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".