Preferences of Persons With or at Risk for Hepatitis C for Long-Acting Treatments
Bibliographic record
Abstract
BACKGROUND: Whereas safe, curative treatments for hepatitis C virus (HCV) have been available since 2015, there are still 58 million infected persons worldwide, and global elimination may require new paradigms. We sought to understand the acceptability of approaches to long-acting HCV treatment. METHODS: A cross-sectional, 43-question survey was administered to 1457 individuals with or at risk of HCV at 28 sites in 9 countries to assess comparative interest in a variety of long-acting strategies in comparison with oral pills. RESULTS: Among HCV-positive participants, 37.7% most preferred an injection, 5.6% an implant, and 6% a gastric residence device, as compared with 50.8% who stated they would most prefer taking 1-3 pills per day. When compared directly to taking pills, differences were observed in the relative preference for an injection based on age (P<.001), location (P<.001), and prior receipt of HCV treatment (P=.005) but not sex. When an implant was compared with pills, greater preference was represented by women (P=.01) and adults of younger ages (P=.01 per 5 years). Among participants without HCV, 49.5% believed that injections are stronger than pills and 34.7% preferred taking injections to pills. Among those at-risk participants who had received injectable medications in the past, 123 of 137 (89.8%) expressed willingness to receive one in the future. CONCLUSIONS: These data point to high acceptability of long-acting treatments, which for a substantial minority might even be preferred to pills for the treatment of HCV infection. Long-acting treatments for HCV infection might contribute to global efforts to eliminate hepatitis C.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".