Public values and guiding principles for implementing epitope compatibility in kidney transplantation allocation criteria: results from a Canadian online public deliberation
Bibliographic record
Abstract
Abstract Background Epitope compatibility in deceased donor kidney allocation is an emerging area of precision medicine (PM), seeking to improve compatibility between donor kidneys to transplant candidates in the hope of avoiding kidney rejection. Though the potential benefits of using epitope compatibility are promising, the implied modification of deceased organ allocation criteria requires consideration of significant clinical and ethical trade-offs. As a matter of public policy, these trade-offs should consider public values and preferences. We invited members of the Canadian public to participate in a deliberation about epitope compatibility in deceased donor kidney transplantation; to identify what is important to them and to provide recommendations to policymakers. Methods An online public deliberation was conducted with members of the Canadian public, in which participants were asked to construct recommendations for policymakers regarding the introduction of epitope compatibility to kidney allocation criteria. In the present paper, a qualitative analysis was conducted to identify the values reflected in participants’ recommendations. All virtual sessions were recorded, transcribed, and analyzed using NVivo 12 software. Results Thirty-two participants constructed nine recommendations regarding the adoption of epitope compatibility into deceased donor kidney allocation. Five values were identified that drove participants’ recommendations: Health Maximization, Protection/Mitigation of Negative Impacts, Fairness, Science/Evidence-based Healthcare, and Responsibility to Maintain Trust. Conflicts between these values were discussed in terms of operational principles that were required for epitope compatibility to be implemented in an acceptable manner: the needs for Flexibility, Accountability, Transparent Communication and a Transition Plan. All nine recommendations were informed by these four principles. Participant deliberations were often dominated by the conflict between Health Maximization and Fairness or Protection/Mitigation of Negative Impacts, which was discussed as the need for Flexibility. Two additional values (Efficient Use of Resources and Logic/Rationality) were also discussed and were reasons for some participants voting against some recommendations. Conclusions Public recommendations indicate support for using epitope compatibility in deceased donor kidney allocation. A flexible approach to organ allocation decision-making may allow for the balancing of Health Maximization against maintaining Fairness and Mitigating Negative Impacts. Flexibility is particularly important in the context of epitope compatibility and other PM initiatives where evidence is still emerging.
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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 | 0.068 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.025 | 0.015 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".