Between a rock and a hard place: the incommensurate ethics of emotionally-related living organ donation
Bibliographic record
Abstract
At the end of 2007, over 71,000 candidates in the United States were awaiting a kidney transplant. That same year, 16,622 kidney transplants took place [1]. The growing shortage of organs in the face of escalating need has placed pressure on transplant centers to accept organs from voluntary living donors. Emotionally- related living organ donation (ERLOD) is becoming increasingly common. In ERLOD, donors and recipients are genetically unrelated but linked by close emotional ties. In the case of kidney transplants, ERLOD achieved over 90% success rates after only one year [2]. However, the significant need and efficacy of this practice are not sufficient for its justification; this program must also be ethically acceptable [3]. Living organ donation in general raises concerns regarding the acceptable standards of medical practice and ERLOD in particular poses unique challenges. This article examines, within a clinical care framework, the ethical concerns surrounding ERLOD and why these concerns may be difficult to reconcile from this perspective alone. Discussion may benefit from using the ethical framework of clinical research in adjunction with the clinical care framework to offer a more flexible scope of analysis.
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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 | 0.031 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.087 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.002 | 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".