Pegylated-Interferon Alpha 2b and Ribavirin for Recurrent Hepatitis C after Liver Transplantation: From a Canadian Experience to Recommendations for Therapy
Bibliographic record
Abstract
BACKGROUND: Recurrent hepatitis C (HCV) after liver transplantation (LT) is often more aggressive and treatments tend to be less successful. Pegylated-interferon and ribavirin are the standard of care for the treatment of HCV; however, there is limited published experience of its use after LT. OBJECTIVE: To report the results of pegylated-interferon alpha 2b (PEG-IFN) plus ribavirin for the treatment of recurrent HCV after LT and compare the results with published data. METHODS: Thirteen patients with recurrent HCV were treated with PEG-IFN plus ribavirin. Liver biopsies demonstrated early-stage disease in eight patients and advanced fibrosis in five patients. The average starting dose of PEG-IFN was 0.91 microg/kg (range 0.5 microg/kg to 1.1 microg/kg) per week and ribavirin was started at 662 mg (range 0 mg to 1200 mg) per day. PEG-IFN treatment began an average of 24 months after LT (range six to 73 months). The dose of PEG-IFN was increased in four patients but only two reached 1.5 microg/kg. The ribavirin dose was increased in four, reduced in six and only seven patients reached a ribavirin dose greater than 10.6 mg/kg. RESULTS: A sustained virological response was seen in four of 13 (30.7%) patients and in four of eight (50%) patients with early-stage disease compared with zero of five patients with advanced fibrosis (P=0.1). Cytopenias were common and therapy was poorly tolerated in four of five patients with advanced fibrosis, including acute cellular rejection in three, renal failure in two, liver decompensation in four and death in three. CONCLUSIONS: Although a reasonable sustained virological response can be achieved with the use of PEG-IFN and ribavirin, the treatment is very poorly tolerated by patients with advanced-stage recurrent HCV. Treatment should be instituted before the development of significant fibrosis after LT.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".