Interferon-Based Combination Anti-Viral Therapy for Hepatitis C Virus After Liver Transplantation: A Review and Quantitative Analysis
Bibliographic record
Abstract
Recurrence of hepatitis C virus (HCV) infection after liver transplantation (LT) is universal. However, the efficacy, tolerability and safety of combination interferon and ribavirin (IFN-RIB) or peginterferon and ribavirin (PEG-RIB) anti-viral therapies post-LT are uncertain. We performed a comprehensive search of major medical databases (1980-2005) and conference proceedings (1996-2005). The main outcome measure was sustained virological response (SVR, undetectable HCV RNA) at 6 months. Summary estimates were calculated using random-effects models. Twenty-seven IFN-RIB and 21 PEG-RIB studies were included. IFN-RIB was associated with a pooled SVR rate of 24% (95% CI, 20-27%), while PEG-RIB was associated with an SVR rate of 27% (23-31%). Pooled discontinuation rates were 24% (21-27%) with IFN-RIB and 26% (20-32%) with PEG-RIB. The pooled rate of acute graft rejection was 2% (1-3%) with IFN-RIB and 5% (3-7%) with PEG-RIB. IFN-RIB and PEG-RIB therapies in HCV infection post-LT were associated with similar but overall low SVR and were poorly tolerated. The rate of acute rejection was small. The therapeutic advantage of PEG-RIB therapy observed in non-transplant chronic HCV infection appears to be attenuated post-LT. Clinical trials are needed to evaluate reasons for this post-transplant therapeutic disadvantage and to find strategies to ameliorate them.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".