Is it ethical to invite compatible pairs to participate in exchange programmes?
Bibliographic record
Abstract
Living kidney transplantation offers the best results for patients with end-stage renal disease (ESRD). This form of transplantation is no longer restricted to genetically or emotionally related donors, as shown by the acceptance of non-directed living anonymous donors, and the development of exchange programmes (EPs). EPs make it possible to perform living kidney transplantation among incompatible pairs, but while such programmes can help increase living organ donation, they can also create a degree of unfairness. Kidney transplant recipients in the O blood group are at a disadvantage when it comes to EPs because they can only receive organs from O donors, whereas O donors are universal donors. This poses a major challenge in terms of distributive justice and equity. A way to remedy this situation is through altruistic unbalanced paired kidney exchange (AUPKE), in which a compatible pair consisting of an O blood group donor and a non-O recipient is invited to participate in an EP. Although the AUPKE approach appears fairer for O recipients, it still raises ethical questions. How does this type of exchange affect the donor/recipient gift relationship? Should recipients in compatible pairs receive a 'better organ' than the one they would otherwise have received from their intended donor? Finally, what is the role of transplant teams in AUPKE? This article will examine the organisational and ethical challenges associated with EPs and AUPKE, and compare different EP policies in countries where such programmes exist.
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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 | 0.052 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".