Direct-Acting Antivirals for Hepatitis C Virus (HCV): The Progress Continues
Bibliographic record
Abstract
Treatment for hepatitis C virus (HCV) infection has progressed at remarkable speed. From poorly tolerated injectable therapy with very low cure rates, treatment has moved to highly effective well-tolerated all oral direct-acting antiviral therapies with cure rates above 90% for almost all patients populations. Direct-acting antivirals have developed out of an improved understanding of the viral lifecycle with recognition of targets that could be inhibited by small molecules. To date protease inhibitors, non-structural 5a inhibitors and nucleotide and non-nucleotide polymerase inhibitors have been developed. These agents have been used initially with peginterferon and ribavirin and subsequently in combination without the need for interferon. Rational combinations have overcome the major challenge of rapid emergence of drug resistance and second-generation agents in each class have improved safety and efficacy profiles with fewer drug-drug interactions and very few adverse effects. The progress of direct-acting antiviral development is outlined with a review of each class of agent as well as a discussion of challenges for the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".