Indicators and predictors of response to anti‐viral therapy in chronic hepatitis C
Bibliographic record
Abstract
The complications of chronic hepatitis C, including cirrhosis and hepatocellular carcinoma, are expected to increase dramatically world-wide over the next 10-20 years. Immunomodulatory/anti-viral therapy, employing interferon alfa both alone and in combination with ribavirin, affords the only effective treatment for hepatitis C. Accurate early prediction of response to interferon therapy may decrease or eliminate unnecessary or ineffective treatment, permit greater flexibility in tailoring therapy on an individual basis, and enhance the cost-effectiveness of treatment. Liver biopsy provides valuable information about the baseline severity and subsequent progression of hepatitis C. Severe fibrosis or cirrhosis on the pre-treatment liver biopsy is associated with decreased response rates. The measurement of viral RNA levels and genotyping may be used to optimize individual patient treatment. Genotype non-1 and a low viral load are the most significant pre-treatment indicators of sustained virological response. The most reliable predictor of a poor virological response is continued seropositivity for viral RNA during therapy. Therefore, a decision to stop or continue treatment can be based on a positive viral RNA test at 12 weeks for interferon-naive patients receiving interferon or pegylated interferon therapy.
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) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".