All living donors should not be treated equally
Bibliographic record
Abstract
We would like to thank Drs. Udgiri, Kashyap, and Minz for their letter. We agree that motivation should be a part of routine psychosocial evaluation of living donors. However, even though “motivated donors are prepared to take more risks than others,” we do not believe that the transplant team should necessarily permit them to do so. As we argued in our article, “It is not enough to say that a competent adult has the right to take risks because this ignores the fact that during the operation the donor becomes a patient” (1). The suggestion of Drs. Udgiri, Kashyap, and Minz that quantification of motivation be used to help donors and transplant teams decide how much risk deserves further study. However, we would propose that altruistic donors be held to a higher standard than family members in allowing donation based not on a quantitative but rather a qualitative difference. Because the former do not share the same interests with recipients as the latter, there is a greater risk of harm to altruistic donors in allowing them to donate. In contrast, the sharing of interests means that there is a greater risk of harm to an intimate family member in prohibiting him or her from donating than there is to an altruistic donor (2). Finally, the reference that Drs. Udgiri, Kashyap, and Minz provide regarding the transplant community’s decision to accept “more marginal donors to expand the donor pool” is important (3). Unfortunately, although kidneys from expanded-criteria cadaver donors are now widely used for renal trans-plantation in the United States, this alone does not and will not solve the donor organ shortage. To that end, many have turned to expanding the pool of living donors (4,5). Nevertheless, we must do so with caution. Ross has argued elsewhere that “the moral acceptability of physically harming a healthy individual in order that she can donate an organ to another, even if emotionally related, would be less clear cut in a world in which there were enough alternate sources of organs such that healthy individuals did not need to take such risks, whether for their siblings or for members of the wider community” (6). We support continued efforts to locate alternative sources of organs, “whether from cadavers, animal-human hybrids or stem cells” (6). Lainie Friedman Ross Walter Glannon Michelle Josephson J. Richard Thistlethwaite
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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 | 0.019 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.016 | 0.033 |
| Insufficient payload (model declined to judge) | 0.015 | 0.012 |
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".