Hepatitis C virus–infected kidney waitlist patients: Treat now or treat later?
Bibliographic record
Abstract
Currently many but not all centers transplant hepatitis C virus (HCV) viremic positive (+) donor kidneys into HCV+ recipients. Directed donation of HCV+ organs reduces the wait time to transplantation for HCV+ patients. Direct-acting antiviral (DAA) therapy can cure HCV in virtually all who are infected. Some have suggested that treatment of HCV+ waitlisted patients be deferred with the hope that earlier transplantation will provide better outcomes than early DAA therapy. However, there are not enough organs to guarantee prompt transplantation for the current waitlist of infected candidates. A Markov medical decision analysis model was created to compare the overall outcomes of delayed DAA therapy (Option 1) to immediate DAA therapy (Option 2) in waitlisted HCV+ patients. Option 1 patients were modeled to be transplanted 1 year earlier, with a higher cumulative transplant incidence (54% at 5 years post-listing vs 45% for Option 2). Despite this, Option 2 provided 0.43 (95% confidence interval [CI] 0.38-0.49) more life years than Option 1. However, Option 1 was preferred for regions with much greater access to HCV+ organs or in patients with very low HCV+-associated mortality. The best option from an individual patient's perspective will differ by region and candidate.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".