Bibliographic record
Abstract
Every year, the United States receives 35,000 new requests for kidney transplants. Kidney reserves, however, can supply only 17,000 procedures per year. Over time, this difference between need and supply has produced a wait list including roughly 100,000 patients hoping to receive kidneys, many of whom will die on the wait list. As a result, the time a patient is expected to remain on the wait list is less than the life expectancy of someone with kidney failure. What would it take for you to agree to donate a kidney? Direct payment? A tax incentive? Free health care coverage? A “club membership” guarantee that you would be the first to receive an organ if you needed it? Nicola Lacetera, a professor at the University of Toronto’s Institute for Management and Innovation, has found that U.S. residents are likely to support the provision of governmental incentives directed toward living donors. Specifically, Lacetera found that 50 percent of the over 3,000 people surveyed supported providing incentives to donors. In addition, 70 percent of participants surveyed agreed that the government should pay living donors if it were certain that doing so would increase the supply of organs. Providing incentives would not just increase access to organs by a marginal percentage. It would eliminate the kidney transplant wait list. Economists estimate that paying donors $15,000 to $30,000 for their organs would eliminate the 100,000-person wait list within a few years. Providing incentives to living organ donors has become a pressing policy question, with recent scientific developments rendering donation less invasive. Some countries have chosen to provide incentives to live organ donors. In Israel, for example, a live organ donor not only receives a refund for lost earnings from the time necessary for the procedure and recovery, but also receives refunds on medical and life insurance, and does not have to pay the national health tax for three years. Lacetera states that, in the United States, a federal appeals court recently concluded that it was not illegal to pay blood stem cell donors, although payment for organ donation is criminalized. Why has the United States not adopted a new policy, like Israel? Lacetera argues that, despite the support indicated in the survey, people are generally opposed to providing incentives to living organ donors for two reasons. First, we may be concerned with the implementation of any donation incentive program. Second, although we might support the program in theory, the thought of actually adopting one is a “repugnant transaction.” In a case recently before the U.S. Court of Appeals for the Ninth Circuit, parents of children who had leukemia and other diseases that can be alleviated with bone marrow transplants sued Eric Holder, the then-U.S. Attorney General, arguing that the National Organ Transplant Act was unconstitutional in its denial of payments for bone marrow transplants. A nonprofit organization, More Marrow Donors (MMD), which aims to pay donors for their bone marrow, joined the lawsuit. MMD proposed a program in which, through a new extraction operation, removal of the complete fatty substance of the bone marrow would not be necessary. Instead, the valuable blood stem cells from the bone marrow could be collected and separated from the rest of the blood stream. MMD sought to provide donation incentives by awarding donors $3,000 in the form of scholarships, housing allowances, or gifts to charities. In other words, the donor would not receive the funding directly. The National Organ Transplant Act, however, criminalized payment for organ donations, which included bone marrow. MMD argued that prohibiting such payments is unconstitutional. Furthermore, Lacetera argues that we might be concerned with adverse selection in the donor incentive program. As Lacetera explains, the people who might benefit most from the incentives may also be those who “would be more likely to carry transmissible diseases such as hepatitis.” A system of in-kind rewards and delayed compensation, however, like MMD’s proposed housing allowances, would address these concerns. In considering whether an organ incentive program could be coercive, the court explained that “Congress may have been concerned that if donors could be paid, rich patients or the medical industry might induce poor people to sell their organs, even when the transplant would create excessive medical risk, pain, or disability for the donor.” Still, effective policy and regulation could address these concerns. Lacetera argues these concerns are “amenable to ‘tradeoff thinking,’” in which the downsides could “be weighed against the potential efficiency gains that payments may cause,” such as an increase in organ supply. Lacetera found that the average respondent supported a policy in which a public agency controls payment for donations and allocation of organs if the end result is a six percent increase in the organ supply. If we such a system were unregulated, then the average respondent would want a 20 percent increase in organ supply to justify the potential downsides of such a program. Lacetera argues the there may be deeper concerns, however. Providing financial incentives to organ donors may violate our “sacred values,” which Lacetera defines as “principles that individuals and societies are not willing to compromise against any other form of potential gain.” The Ninth Circuit anticipated this moral revulsion. As the court observed, “[p]eople tend to have an instinctive revulsion at denial of bodily integrity, particularly removal of flesh from a human being for us by another,” and this revulsion is most acute with “‘commodification’ of such conduct, that is, the sale of one’s bodily tissue.” In the end, the court did not need to decide the broad policy questions of the legality of organ donation incentives. Instead, the court held that blood stem cells, which are extracted through the blood stream rather than the bone marrow, were not considered organs. Compensating individuals for donating their bone marrow was legal. The Department of Health and Human Services responded to the case by announcing that it would undertake a regulatory proceeding to redefine bone marrow to include the kinds of extraction methods discussed in that case, essentially overruling the appellate court’s decision.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".