Addressing the Shortage of Kidneys for Transplantation: Purchase and Allocation Through Chain Auctions
Bibliographic record
Abstract
Transplantation is generally the treatment of choice for those suffering from kidney failure. Not only does transplantation offer improved quality of life and increased longevity relative to dialysis, it also reduces end-stage renal disease program expenditures, providing savings to Medicare. Unfortunately, the waiting list for kidney transplants is long, growing, and unlikely to be substantially reduced by increases in the recovery of cadaveric kidneys. Another approach is to obtain more kidneys through payment to living "donors," or vendors. Such direct commodification, in which a price is placed on kidneys, has generally been opposed by medical ethicists. Much of the ethical debate, however, has been in terms of commodification through market exchange. Recognizing that there are different ethical concerns associated with the purchase of kidneys and their allocation, it is possible to design a variety of institutional arrangements for the commodification of kidneys that pose different sets of ethical concerns. We specify three such alternatives in detail sufficient to allow an assessment of their likely consequences and we compare these alternatives to current policy in terms of the desirable goals of promoting human dignity, equity, efficiency, and fiscal advantage. This policy analysis leads us to recommend that kidneys be purchased at administered prices by a nonprofit organization and allocated to the transplant centers that can organize the longest chains of transplants involving willing-but-incompatible donor-patient dyads.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".