Organ Donation After Medical Aid in Dying: An Ethical Overview
Bibliographic record
Abstract
Organ Donation after Medical Aid in Dying (OD-MAiD) is currently practised in four countries: Belgium, Canada, the Netherlands, and Spain. While OD-MAiD shares some similarities with MAiD (absent the possibility of organ donation) and with standard organ donation protocols, the combination of OD and MAiD involves unique circumstances that present novel ethical challenges. These challenges revolve around donors' consent and protection, the dead donor rule, and organ allocation. This paper explores these moral challenges and proposes strategies to ensure ethical safeguards in the context of OD-MAiD. An underlying question is whether OD-MAiD, if permitted, should follow the ethical guidelines of living donation or deceased donation, as these two practices commonly operate under distinct moral paradigms. While the living donation paradigm is centred on the protection of donors' interests and emphasises individual choice by allowing donors to decide who receives their organs, the deceased donation framework places more emphasis on enabling recipients to benefit from transplant, and organ allocation is typically based on impartiality. OD-MAiD also raises ethical concerns about how the possibility of donation could influence a patient's decision to seek euthanasia and/or interfere with optimal end-of-life care. Proposing organ donation to individuals considering MAiD could conceivably create pressure to proceed with euthanasia, either to realise a social good or to satisfy the needs of loved ones (if a family member requires an organ). This may undermine the patient's autonomy or well-being at the end of life.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".