How Should We Use Age to Ration Health Care? Lessons from the Case of Kidney Transplantation
Bibliographic record
Abstract
Competing visions for health reform in the United States and renewed interest in health technology assessment (HTA) have led to fierce national debates about the appropriateness of rationing. Because of a limited supply of organs, kidney transplantation has always required rationing and overt discussion of the ethics that guide it, but the field of transplantation has also contended recently with internal calls for a new rationing system. The aim of the Life Years from Transplantation (LYFT) proposal is to allocate kidneys to patients who obtain the greatest survival benefit from transplantation, which would lengthen the lives of kidney transplant recipients but restrict the ability of older Americans to obtain a transplant. The debate around the LYFT proposal reveals the ethical and policy challenges of identifying which patients should receive a treatment based on the results of cost-effectiveness and other HTA studies. This article argues that attempts to use HTA for healthcare rationing are likely to disadvantage older patients. Guiding principles to help ensure that resources such as kidneys are justly allocated across the life span are proposed.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| 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".