Improving institutional fairness to live kidney donors: donor needs must be addressed by safeguarding donation risks and compensating donation costs
Bibliographic record
Abstract
The number of kidney transplants from live donors is increasing worldwide, yet donor needs have not been satisfactorily addressed in either developed or developing countries. This paper argues that unmet donor needs are unfair to live kidney donors in two ways. First, when safeguards against the risks of donation are insufficient, live donation can impair the donor's health and thus his or her fair opportunities to access jobs and offices and to function as a free and equal citizen more generally. Secondly, when the financial costs of donation are not fully compensated, operational fairness (associated with the nephrectomy event) is compromised for the donor. The donor assumes the risks of a nontherapeutic intervention--for the good of the recipient and society--and should not have to incur costs for donating. Based on a systematic analysis of unmet donor needs in developed and developing countries, context-relative measures to improve institutional fairness to live kidney donors are delineated in this paper. The identified ways of safeguarding donation risks and compensating donation costs are not merely means to removing disincentives for donation and increasing donation rates. They are essential for preserving institutional fairness in the health care of the live kidney donor.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".