Key Elements on the Pathway to HCV Elimination: Lessons Learned From the AASLD HCV Special Interest Group 2020
Bibliographic record
Abstract
With a decade left to reach the ambitious goals for viral hepatitis elimination set out by the World Health Organization, many challenges remain. Despite the remarkable improvements in therapy for hepatitis C virus (HCV) infection, most people living with the infection remain undiagnosed, and only a fraction have received curative therapy. Accordingly, the 2020 HCV Special Interest Group symposium at the annual American Association for the Study of Liver Diseases Liver Meeting examined policies and strategies for the scale-up of HCV testing and expanded access to HCV care and treatment outside the specialty setting, including primary care and drug treatment and settings for care of persons who inject drugs and other marginalized populations at risk for HCV infection. The importance of these paradigms in elimination efforts, including micro-elimination strategies, was explored, and the session also included discussion of hepatitis C vaccine development and other strategies to reduce mortality through the use of organs from HCV-infected organ donors for HCV-negative recipients. In this review, the key concepts raised at this important symposium are summarized.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".