The Cost-Effectiveness of Using Payment to Increase Living Donor Kidneys for Transplantation
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: For eligible candidates, transplantation is considered the optimal treatment compared with dialysis for patients with ESRD. The growing number of patients with ESRD requires new strategies to increase the pool of potential donors. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Using decision analysis modeling, this study compared a strategy of paying living kidney donors to waitlisted recipients on dialysis with the current organ donation system. In the base case estimate, this study assumed that the number of donors would increase by 5% with a payment of $10,000. Quality of life estimates, resource use, and costs (2010 Canadian dollars) were based on the best available published data. RESULTS: Compared with the current organ donation system, a strategy of increasing the number of kidneys for transplantation by 5% by paying living donors $10,000 has an incremental cost-savings of $340 and a gain of 0.11 quality-adjusted life years. Increasing the number of kidneys for transplantation by 10% and 20% would translate into incremental cost-savings of $1640 and $4030 and incremental quality-adjusted life years gain of 0.21 and 0.39, respectively. CONCLUSION: Although the impact is uncertain, this model suggests that a strategy of paying living donors to increase the number of kidneys available for transplantation could be cost-effective, even with a transplant rate increase of only 5%. Future work needs to examine the feasibility, legal policy, ethics, and public perception of a strategy to pay living donors.
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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 | 0.013 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".