Systematic review: anti-viral therapy of recurrent hepatitis C after liver transplantation
Bibliographic record
Abstract
BACKGROUND: Hepatitis C virus (HCV) infection is the first cause of liver transplantation worldwide. Recurrence of infection is constant, and compromises patient and graft survival. AIM: To provide an updated review of the main treatments of recurrent HCV. METHODS: MEDLINE (1990 to August 2010) and national meeting abstract search. Search terms included hepatitis C, liver transplantation, treatment, sustained virological response. An emphasis was placed on randomised trials. RESULTS: Anti-viral therapy based on pegylated interferon and ribavirin must be considered before liver transplantation, but is poorly tolerated and has poor results in patients with cirrhosis and end-stage liver disease or hepatocellular carcinoma. Anti-viral therapy can be administrated systematically early after liver transplantation, or in patients with established recurrent chronic hepatitis. Combination of pegylated interferon alpha plus ribavirin results in a sustained virological response of up to 30% in patients with histological HCV recurrence. The results of a small trial of polyclonal anti-HCV to prevent recurrence were disappointing. CONCLUSIONS: Currently available anti-viral therapy is effective only in a minority of transplanted patients infected with HCV. Specifically targeted anti-viral therapies combining interferon alpha and ribavirin, or a combination of antiprotease and antipolymerase components, associated with a genetic prediction of anti-viral response and blocking HCV cell entry should improve the long-term prognosis of recurrent hepatitis C in the near future.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".