Intrapatient viral diversity and treatment outcome in patients with genotype 3a hepatitis C virus infection on sofosbuvir‐containing regimens
Bibliographic record
Abstract
Treatment with the direct-acting antiviral agent (DAA) sofosbuvir (SOF), an NS5B inhibitor, and velpatasvir (VEL), an NS5A inhibitor, demonstrates viral cure rates of ≥95% in hepatitis C virus (HCV) genotypes (GT) 1-6. Here, we investigated intrapatient HCV diversity in NS5A and NS5B using Shannon entropy to examine the relationship between viral diversity and treatment outcome. At baseline, HCV diversity was lowest in patients infected with HCV GT3 as compared to the other GTs, and viral diversity was greater in NS5A than NS5B (P < .0001). Treatment outcome with SOF/VEL or the comparator regimen of SOF with ribavirin (RBV) was not correlated with baseline diversity. However, among persons treated with SOF/VEL, a decrease in diversity from baseline was observed at relapse in the majority virologic failures, consistent with a viral bottleneck event at relapse. In contrast, an increase in diversity was observed in 27% of SOF+RBV virologic failures. We investigated whether the increase in diversity was due to an increase in the transition rate, one mode of potential RBV-mediated mutagenesis; however, we found no evidence of this mechanism. Overall, we did not observe that viral diversity at baseline influenced treatment outcome, but the diversity changes observed at relapse can improve our understanding of RBV viral suppression in vivo.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".