Hepatitis C in Patients With Minimal or No Hepatic Fibrosis: The Impact of Treatment and Sustained Virologic Response on Patient-Reported Outcomes
Bibliographic record
Abstract
Background: While the necessity of treatment of hepatitis C virus (HCV)-infected patients with advanced liver disease is widely accepted, the benefit of treating patients without significant liver disease is less well established. Our aim was to assess the effect of treating HCV in patients with no or minimal fibrosis (Metavir stage F0-F1) on patient-reported outcomes (PROs). Methods: HCV-infected patients with F0-F1 from 16 clinical trials were included. PROs were collected before, during, and after treatment. Results: A total of 1548 HCV-infected patients with F0-F1 were included (mean age 46 years, 43% male, 81% treatment-naive). Patients were treated with interferon (IFN) + sofosbuvir (SOF) + ribavirin (RBV) (n = 91) or SOF + RBV with or without ledipasvir (n = 479) or IFN- and RBV-free regimens with SOF + ledipasvir or SOF + velpatasvir or SOF + velpatasvir + voxilaprevir (n = 978). By the end of treatment, patients receiving IFN-containing regimens experienced significant decreases in most PRO domains (-4.5 to -28.7 on a 0-100 scale), while subjects treated with IFN-free RBV-containing regimens had a modest impairment (-2.3 to -8.9) (P ≤ .01). In contrast, treatment with regimens without IFN and RBV led to PRO improvements (+1.2 to +10.9). Regardless of the regimen, sustained virologic responses (SVRs) at 12 and 24 weeks were universally associated with PRO improvements (+2.1 to +14.7, P < .0001. Conclusions: HCV-infected subjects with no or minimal fibrosis treated with IFN- and RBV-free regimens experienced on-treatment and post-SVR PRO improvements.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".