Policy Challenges for Organ Allocation in an Era of “Precision Medicine”
Bibliographic record
Abstract
There is increasing interest in the use of precision medicine tools and evidence-based outcome measures for donor-recipient matching to optimize transplant outcomes. Although the shift toward greater precision can provide health and resource benefits, it may be perceived as conflicting with both established equity-focused organ allocation norms and the legal and ethical obligations of health care providers and related institutions. With increasing evidence that various forms of human leukocyte antigen (HLA) mismatch and/or prognostic biomarkers can affect outcomes, the tension between maximizing utility and ensuring equity seems likely to intensify. In Canada, health care providers are generally required by law to put the interests of their patient, such as access to an organ, above the needs of the health care system and other patients. In addition, transplantation right of access lawsuits, which have been successful in the past, could affect the implementation of precision approaches. These legal tensions could be further heightened by media representations, which have historically favored strong rights of access. When implementing new precision technologies in organ allocation, there will be a recurrent need for policymakers to revisit the balance of equity and utility and to assess how to craft rules that reflect our society's conception of a fair allocation system.
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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 | 0.025 | 0.069 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.042 | 0.052 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".