Bibliographic record
Abstract
A key to effective interferon- (IFN-) free therapy for hepatitis C virus (HCV) infection is a direct-acting antiviral (DAA) with a high barrier to resistance that can act as the backbone to any regimen. Ideally, this agent should also be active against all HCV genotypes, be well tolerated and have few drug interactions. Nucleoside/nucleotide analogues (NAs) that inhibit the function of the HCV RNA-dependent-RNA polymerase fit these requirements and thus hold promise as a cornerstone for new IFN-free regimens. To date, the issue with this class of agents has been toxicity. Numerous NAs in early clinical development have led to significant toxicity leading to their abandonment. However, sofosbuvir, a prodrug of a uridine NA, has moved through development with a clean-safety profile leading to its recent approval. When combined with ribavirin (RBV) alone, sofosbuvir is effective against genotype 2 and even genotype 3 if duration is extended. There are currently limited data with this combination in genotype 1; however, when sofosbuvir is combined with other DAAs of different classes, it is highly effective in almost all patients. To date, sofosbuvir has been studied with protease, NS5A, and nonnucleoside HCV polymerase inhibitors, including as part of a fixed-dose combination single tablet with the NS5A inhibitor ledipasvir, with very high rates of SVR with as little as 8 weeks of therapy. Combining two DAAs to sofosbuvir may shorten therapy even further. Because of the poor replicative fitness of the S282T sofosbuvir-resistant variant, resistance to sofosbuvir has not been a significant clinical issue in trials thus far. In addition to sofosbuvir, other NAs are in early-stage development. Provided unanticipated toxicity does not emerge, NAs are likely to play a major role as a backbone for future HCV therapy. The rationale for using this class of agents and the clinical data available to date are reviewed.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".