Critical challenges and emerging opportunities in hepatitis C virus research in an era of potent antiviral therapy: Considerations for scientists and funding agencies
Bibliographic record
Abstract
The development and clinical implementation of direct-acting antivirals (DAAs) has revolutionized the treatment of chronic hepatitis C. Infection with any hepatitis C virus (HCV) genotype can now be eliminated in more than 95% of patients with short courses of all-oral, well-tolerated drugs, even in those with advanced liver disease and liver transplant recipients. DAAs have proven so successful that some now consider HCV amenable to eradication, and continued research on the virus of little remaining medical relevance. However, given 400,000 HCV-related deaths annually important challenges remain, including identifying those who are infected, providing access to treatment and reducing its costs. Moreover, HCV infection rarely induces sterilizing immunity, and those who have been cured with DAAs remain at risk for reinfection. Thus, it is very unlikely that global eradication and elimination of the cancer risk associated with HCV infection can be achieved without a vaccine, yet research in that direction receives little attention. Further, over the past two decades HCV research has spearheaded numerous fundamental discoveries in the fields of molecular and cell biology, immunology and microbiology. It will continue to do so, given the unique opportunities afforded by the reagents and knowledge base that have been generated in the development and clinical application of DAAs. Considering these critical challenges and new opportunities, we conclude that funding for HCV research must be sustained.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".