Comments on “Proposed US Legislation to Pay Kidney Donors: Counter-productive and Against Global Ethical Standards”
Bibliographic record
Abstract
In a recent article, Capron et al1 again decried consideration of a reward for kidney donation—in this case, a bill introduced in Congress (H.R.9275) to provide a tax credit to nondirected donors. Yet they accept that there is a shortage of kidneys, do not provide an alternative to increase donation, and, rather than providing a rational argument to make their point, engage in fearmongering, for example, that the bill, which proposes a government-regulated system, would be followed by a “slippery slope” leading to acceptance of individuals buying kidneys, and a competitive increase in price. To support their position, Capron et al provided misleading data, stating we do not need H.R.9275 because donation rates in the United States have risen from 4730 in 1999 to 6866 in 2019. However, they do not acknowledge that all of this increase occurred before 2002 (n = 6241); subsequently, rates have not changed (n = 6292 in 2023),2 and 6000–7000 living donors each year for the past 2 decades have not solved the organ shortage problem. The authors suggest that creating financial neutrality for donors will be sufficient to significantly increase donations. Yet in numerous countries where financial neutrality has been implemented (eg, in Canada, Europe), living donations have not increased and organ shortages continue. The authors state that if the bill is passed, related donors would be “reluctant to provide a kidney for free,” and that many patients would prefer a nondirected donor kidney rather than seeking a kidney from family or friends. However, they do not show why, other than a change from the status quo, this is a problem. If donation rates soar, all candidates benefit, and there will likely still continue to be an advantage—a shorter wait—for donations from family or friends. In addition, currently, 40% of related donors feel pressure (internal and/or external) to donate,3 which is something we think of as a negative when considering informed consent. We know that relatives of those with kidney failure are at risk for kidney failure. To date, Organ Procurement and Transplantation Network and other data suggest that a donor who is a first-degree relative of the recipient is at increased risk.4 Why insist relatives undertake this increased risk? While objecting to H.R.9275, the authors do not provide an alternative option to increase donation, thus accepting that thousands of approved candidates each year will die or become too sick to transplant.2 They argue that H.R.9275 places “personal choice and maximizing transplants above all other values.” There are many “values,” and these often conflict. The bill specifically places “personal choice” (ie, autonomy) and “maximizing transplants” (ie, saving lives) above the alternative—thousands of candidates on the waitlist dying or becoming too sick to transplant. While making their argument, the authors ignore that (1) numerous surveys show that the public favors incentives (reviewed in reference 5), (2) a survey of the ASTS anmembership supported incentives,6 and (3) a joint meeting on the topic by the AST and ASTS concluded that we should eliminate disincentives and move toward trials of incentives.7 Finally, the authors suggest the bill is against global ethical standards. There is no doubt that it is against some people’s standards. However, others worldwide (including many ethicists), especially in the context of the ongoing organ shortage and its consequences, have argued strongly that incentives should be considered.5,8,9
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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 | 0.022 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.109 | 0.075 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".