Timing of hepatitis C virus infection treatment in kidney transplant candidates
Bibliographic record
Abstract
Hepatitis C virus (HCV) infection is prevalent in patients with kidney disease including transplant candidates and recipients. It is associated with increased morbidity and mortality in end-stage renal disease patients and also increases the risk of allograft rejection and decreases allograft and patient survival post-transplant. Newly developed direct acting antivirals have revolutionized the way HCV is treated. Whether patients are treated before or after kidney transplantation, the cure rates with direct acting antivirals are >90%. Great debate has formed revolving the optimal timing to treat kidney transplant candidates. On the one hand, treatment before transplantation decreases early post-transplant complications related to HCV. On the other, postponing treatment until after transplantation opens the possibility of transplanting a kidney from a HCV positive donor, which is associated with shorter waiting time and improved organ utilization by expanding the organ donor pool. Most patients living in an area where waiting time is reduced by accepting an HCV positive kidney would benefit by the strategy of treatment post-transplantation, but this decision needs to be individualized in a patient-by-patient basis given that there are special circumstances (i.e., severe HCV-related extrahepatic manifestations, availability of live donors, etc.) in which treatment before transplant might be preferred.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".