Accepting hepatitis C virus–infected donor hearts for transplantation: Multistep consent, unrealized opportunity, and the Stanford experience
Bibliographic record
Abstract
The current mismatch between supply and demand of organs has prompted transplant clinicians to consider innovative solutions to broaden the donor pool. Advancements of direct-acting antiviral agent (DAA) therapy for hepatitis C virus (HCV) have allowed entertaining the use of viremic donor organs in nonviremic recipients. In this report, we describe the evolution of HCV treatment, ethics and informed consent, cost-effectiveness of HCV medications in treating acute HCV post-transplantation, and the Stanford experience with two HCV-viremic donor heart transplantations. We describe excellent short-term outcomes post-heart transplantation with HCV NAT-positive organs. The availability of this therapy may expand the donor pool. While we await larger-scale clinical data on the effectiveness and safety of DAA therapy in patients after heart transplantation, many transplant centers have already started accepting organs from HCV-infected donors, balancing the unknown long-term risks versus the benefits of shorter wait times and expansion of the donor pool. Protocols and multidisciplinary teams are needed to effectively communicate risk to potential recipients, to ensure timely DAA access, and to implement appropriate clinical follow-up in order to achieve excellent clinical outcomes and to maximize the donor pool by utilizing HCV-infected organs for heart 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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".