Letter to the Editor: The urgent need for consensus around organ donation after assisted dying
Bibliographic record
Abstract
To the editor, Glinka et al’s review of liver transplantation after medical assistance in dying adds to the ever-growing literature from countries and states where voluntary assisted dying (VAD) or euthanasia is legal.1 Outcomes are good, and warm ischemic times are short; clearly, it works. However, opinions are likely to be as divided as those pertaining to the ethics of VAD itself. For hepatologists, the discussion is further layered by a compelling need to expand the donor pool and prolong the lives of patients with liver failure. Ray and Martin have reported that in 2020, 4% of donations followed euthanasia or VAD in the Netherlands and Canada.2 This expansion is highly significant and is no longer theoretical. It is occurring across the globe by degrees, but society chairs, thought leaders, and journal editors have not yet opined, leaving somethin g of a moral and ethical vacuum for clinicians who must now decide what stance to take. Although this is not the place to rehearse all the arguments for and against organ donation after VAD, the “dead donor rule” tends to dominate the discussion. This mandates that organs are only taken from the dead, protecting individuals from harm and society from a dystopian end point.3 The boundary between life and death becomes blurred when death is brought about by the removal of vital organs that are to be donated (so-called organ donation euthanasia).4 It has been argued that patient autonomy should not extend this far due to the wider societal consequences.5 This is but one example of many challenging ethical debates that the transplant community must work through in advance of a growing phenomenon. If guidance, structures, and safeguards are not put in place, we will see variation in practice—a sure sign that risks of injustice and inequity exist.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".