Persistence of Virologic Response after Liver Transplant in Hepatitis C Patients Treated with Ledipasvir / Sofosbuvir Plus Ribavirin Pretransplant
Bibliographic record
Abstract
Recurrence of HCV infection in patients with chronic hepatitis C virus (HCV) at the time of liver transplantation is nearly universal and reduces the likelihood of graft and patient survival. We evaluated outcomes of 17 patients (16 with HCV genotype 1 and 1 with genotype 4) who received up to 12 or 24 weeks of ledipasvir/sofosbuvir plus ribavirin prior to or up to the time of liver transplant in the SOLAR-1 and SOLAR-2 trials. In all patients, HCV RNA was < 15 IU/mL prior to transplant. At screening, 6 patients were Child-Pugh-Turcotte (CPT) class B and 11 were CPT class C. Seven patients underwent transplant prior to completing assigned treatment, with 4 treated for < 12 weeks. The primary endpoint was posttransplant virologic response 12 weeks after transplant (pTVR12) in patients with HCV RNA < 15 IU/mL at their last measurement prior to transplant. Overall, 94% (16/17) achieved pTVR12. All who achieved pTVR12 received at least 11 weeks of treatment. The single patient who did not achieve pTVR12 discontinued study drug on day 21 and underwent liver transplant the following day. The patient had HCV RNA < 15 IU/mL at post-transplant week 2 but died 15 days post-transplant because of multi-organ failure and septic shock. Among a small population of HCV patients with decompensated cirrhosis, virologic response to ledipasvir / so-fosbuvir plus ribavirin prior to liver transplantation was maintained after transplantation, even if treatment was stopped early. Administration of ledipasvir / sofosbuvir plus ribavirin before liver transplant can prevent post-transplant HCV recurrence.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".