Meta‐analysis: re‐treatment of genotype I hepatitis C nonresponders and relapsers after failing interferon and ribavirin combination therapy
Bibliographic record
Abstract
BACKGROUND: The efficacy of re-treating genotype I hepatitis C virus (HCV) patients who failed combination therapy with interferon/pegylated interferon (PEG-IFN) and ribavirin remains unclear. AIMS: To quantify sustained virological response (SVR) rates with different re-treatment regimens through meta-analysis of randomized controlled trials (RCTs). METHODS: Randomized controlled trials of genotype I HCV treatment failure patients that compared currently available re-treatment regimens were selected. Two investigators independently extracted data on patient population, methods and results. The pooled relative risk of SVR for treatment regimens was computed using a random effects model. RESULTS: Eighteen RCTs were included. In nonresponders to standard interferon/ribavirin, re-treatment with high-dose PEG-IFN combination therapy improved SVR compared with standard PEG-IFN combination therapy (RR=1.49; 95% CI: 1.09-2.04), but SVR rates did not exceed 18% in most studies. In relapsers to standard interferon/ribavirin, re-treatment with high-dose PEG-IFN or prolonged CIFN improved SVR (RR=1.57; 95% CI: 1.16-2.14) and achieved SVR rates of 43-69%. CONCLUSIONS: In genotype I HCV treatment failure patients who received combination therapy, re-treatment with high-dose PEG-IFN combination therapy is superior to re-treatment with standard combination therapy, although SVR rates are variable for nonresponders (≤18%) and relapsers (43-69%). Re-treatment may be appropriate for select patients, especially relapsers and individuals with bridging fibrosis or compensated cirrhosis.
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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 | 0.024 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.061 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".