Cost-effectiveness of using kidneys from hepatitis C nucleic acid test–positive donors for transplantation in hepatitis C–negative recipients
Bibliographic record
Abstract
Kidneys from deceased donors who are hepatitis C virus (HCV) nucleic acid test positive are infrequently used for transplantation in HCV-negative patients due to concerns about disease transmission. With the development of direct-acting antivirals (DAAs) for HCV, there is now potential to use these kidneys in HCV-negative candidates. However, the high cost of DAAs poses a challenge to adoption of this strategy. We created a Markov model to examine the cost-effectiveness of using deceased donors infected with HCV for kidney transplantation in uninfected waitlist candidates. In the primary analysis, this strategy was cost saving and improved health outcomes compared to remaining on the waitlist for an additional 2 or more years to receive a HCV-negative transplant. The strategy was also cost-effective with an incremental cost-effectiveness ratio of $56 018 per quality-adjusted life year (QALY) from the payer perspective, and $4647 per QALY from the societal perspective, compared to remaining on the waitlist for 1 additional year. The results were consistent in 1-way and probabilistic sensitivity analyses. We conclude that the use of kidneys from deceased donors with HCV infection is likely to lead to improved clinical outcomes at reduced cost for HCV-negative transplant candidates.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".