108.4: Organ donation after medical assistance in dying: An ethical overview.
Bibliographic record
Abstract
Introduction: Organ Donation after Medical Assistance in Dying (OD-MAiD) is currently practiced in Belgium, Canada, the Netherlands, and Spain. Although OD-MAiD shares characteristics with both traditional MAiD and established organ donation procedures, it presents a unique ethical landscape. The combination of these two practices raises novel concerns related to donor consent, adherence to the dead donor rule, and organ allocation ethics. A key ethical question is whether OD-MAiD should be guided by the norms of living donation, which prioritize donor autonomy, or deceased donation, which prioritizes recipient needs and impartial organ allocation. Method: This study conducts a normative ethical analysis of OD-MAiD by comparing it to established frameworks for living and deceased organ donation. Ethical considerations are explored through conceptual analysis and supported by reference to existing medical and bioethical guidelines in countries where OD-MAiD is practiced. Results: The analysis identifies several ethical tensions unique to OD-MAiD. These include: (1) potential influence of organ donation prospects on a patient’s decision to pursue MAiD, (2) challenges in preserving autonomy and ensuring informed consent under emotionally and medically complex conditions, (3) tensions between donor-directed and system-directed organ allocation, and (4) the risk of compromising end-of-life care in pursuit of organ viability. Conclusion: OD-MAiD poses distinct ethical challenges that require dedicated safeguards beyond those used in standard organ donation or MAiD protocols. Ethical governance of OD-MAiD should aim to preserve patient autonomy, prevent coercion, and strike a careful balance between the two paradigms of living and deceased donation. Policymakers must carefully design frameworks to protect both donor and recipient interests without compromising end-of-life care quality.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| 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".