Hepatitis C in Liver Allograft Recipients: Utility of One-Year Post-Transplantation Biopsy as an Indicator of Antiviral Therapy
Bibliographic record
Abstract
BACKGROUND: All-oral interferon-free regimens for hepatitis C viral (HCV) infection are highly efficacious; however, high cost is a barrier to applicability. Liver allograft recipients are particularly likely to benefit from therapy as HCV often leads to graft dysfunction and loss. In this study, we aimed to establish the utility of allograft biopsy at 1 year post-transplant as an indicator of treatment. METHODS AND RESULTS: Among 252 liver recipients enrolled, 136 (54%) developed severe disease (fibrosing cholestatic hepatitis (FCH) or fibrosis stage ≥ 2 at 1 year post-transplant). Multivariable analysis revealed younger recipient age and female gender, older donor age and T cell depletive therapy to be independent predictors of severe disease. Recipients with severe disease had higher rate of further graft loss compared to those with mild disease. Patients with mild disease and sustained virologic response (SVR) had the best survival rate, whereas those with severe disease and viremia had the worst survival (96% versus 63% at 5 years). CONCLUSION: In conclusion, allograft biopsy at 1 year helps identify recipients at high risk of further graft dysfunction and loss. In view of high cost of therapy, treatment should be preferably directed to high-risk patients including those with FCH or fibrosis stage ≥ 2 by 1 year post-transplant.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".