Impact of disease severity on outcome of antiviral therapy for chronic hepatitis C: Lessons from the HALT-C trial
Bibliographic record
Abstract
In patients with chronic hepatitis C, advanced fibrosis and cirrhosis are associated with lower rates of sustained virologic response (SVR) to interferon (IFN)-based therapy. In this study, we assessed virologic response to retreatment with peginterferon alfa-2a and ribavirin (RBV), as a function of the baseline fibrosis score (Ishak staging) and platelet count, in 1,046 patients enrolled in the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial. All patients had failed prior treatment with IFN or peginterferon +/- RBV and had Ishak fibrosis scores > or = 3. Four groups of patients with increasingly severe liver disease were compared: (A) bridging fibrosis (Ishak 3 and 4) with platelet counts >125,000/mm3 (n = 559); (B) bridging fibrosis with platelet counts < or =125,000/mm3 (n = 96); (C) cirrhosis (Ishak 5 and 6) with platelet counts >125,000/mm3 (n = 198); and (D) cirrhosis with platelet counts < or =125,000/mm3 (n = 193). SVR rates were 23%, 17%, 10%, and 9% in groups A, B, C, and D, respectively (P < .0001 for trend). Reduction in SVR as a function of increasingly severe disease was independent of age, percent African American, HCV genotype, HCV level, and type of prior therapy. Dose reduction lowered SVR frequencies, but to a lesser extent than disease severity. By logistic regression, cirrhosis (P < .0001) was the major determinant that impaired virologic response, independent of dose reduction or platelet count. In conclusion, disease severity is a major independent determinant of rate of SVR in patients with advanced chronic hepatitis C. New strategies are needed to optimize antiviral therapy in these "difficult-to-cure" patients.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".