Hepatitis C virus infection in kidney transplantation‐changing paradigms with novel agents
Bibliographic record
Abstract
Hepatitis C virus (HCV) is a common cause of increased morbidity and mortality in kidney transplant patients. It is associated with posttransplant glomerulonephritis, chronic allograft nephropathy, and New Onset Diabetes after Transplant (NODAT). In the past, HCV was difficult to treat due to the presence of interferon alpha-based therapies that were difficult to tolerate and were associated with adverse side-effects, such as the risk of rejection. With the advent of oral directly acting antiviral therapies, the landscape for HCV and transplantation has changed. These agents are highly effective and well tolerated with minimal side-effects. Sustained viral response rates in excess of 90% are achieved with most current treatment regimens active against all HCV genotypes. These new agents may show an improvement in graft and patient survival while essentially eliminating the risk of acute rejection from the use of prior interferon-based HCV therapies. These agents may also result in an improvement in organ allocation for HCV donor/HCV recipient transplantation. This review is meant to discuss the epidemiology of HCV, the new oral direct-acting antiviral agents (DAAs) and future opportunities for research in the field of HCV related transplantation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".