The Rule of Rescue in the Era of Precision Medicine, HLA Eplet Matching, and Organ Allocation
Bibliographic record
Abstract
Precision medicine can put clinicians in a position where they must act more as resource allocators than their traditional role as patient advocates. In the allocation of transplantable organs and tissues, the use of eplet matching will enhance precision medicine but, in doing so, generate a tension with the present reliance on rule of rescue and justice-based factors for allocations. Matching donor and recipient human leukocyte antigens (HLA) is shown to benefit virtually all types of solid organ transplants yet, until recently, HLA-matching has not been practical and was shown to contribute to ethnic/racial disparities in organ allocation. Recent advances using eplets from the HLA molecule has renewed the promise of such matching for predicting patient outcomes. The rule of rescue in organ allocation reflects a combination of ethical, policy, and legal imperatives. However, the rule of rescue can impede the allocation strategies adopted by professional medical associations and the optimal use of scarce transplant resources. While eplet-matching seeks to improve outcomes, it may potentially frustrate current ethics-motivated initiatives, established patient-practitioner relationships, and functional conventions in the allocation of medical resources such as organ and tissue transplants. Eplet-matching allocation schemes need to be carefully and collaboratively designed with clear, fair and equitable guidelines that complement functional conventions and maintain public trust.
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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 | 0.123 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.075 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.019 | 0.041 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".