Organ donation after medical assistance in dying or cessation of life-sustaining treatment requested by conscious patients: the Canadian context
Bibliographic record
Abstract
In June 2016, following the decision of the Supreme Court of Canada to decriminalise assistance in dying, the Canadian government enacted Bill C-14, legalising medical assistance in dying (MAID). In 2014, the province of Quebec had passed end-of-life care legislation making MAID available as of December 2015. The availability of MAID has many implications, including the possibility of combining this practice with organ donation through the controlled donation after cardiac death (cDCD) protocol. cDCD most often occurs in cases where the patient has a severe neurological injury but does not meet all the criteria for brain death. The donation is subsequent to the decision to withdraw life-sustaining treatment (LST). Cases where patients are conscious prior to the withdrawal of LST are unusual, and have raised doubts as to the acceptability of removing organs from individuals who are not neurologically impaired and who have voluntarily chosen to die. These cases can be compared with likely scenarios in which patients will request both MAID and organ donation. In both instances, patients will be conscious and competent. Organ donation in such contexts raises ethical issues regarding respect for autonomy, societal pressure, conscientious objections and the dead-donor rule. In this article, we look at relevant policies in other countries and examine the ethical issues associated with cDCD in conscious patients who choose to die.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.029 | 0.016 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".