Physician-Assisted Suicide and Euthanasia in the ICU: A Dialogue on Core Ethical Issues*
Bibliographic record
Abstract
OBJECTIVE: Many patients are admitted to the ICU at or near the end of their lives. Consequently, the increasingly common debate regarding physician-assisted suicide and euthanasia holds implications for the practice of critical care medicine. The objective of this article is to explore core ethical issues related to physician-assisted suicide and euthanasia from the perspective of healthcare professionals and ethicists on both sides of the debate. SYNTHESIS: We identified four issues highlighting the key areas of ethical tension central to evaluating physician-assisted suicide and euthanasia in medical practice: 1) the benefit or harm of death itself, 2) the relationship between physician-assisted suicide and euthanasia and withholding or withdrawing life support, 3) the morality of a physician deliberately causing death, and 4) the management of conscientious objection related to physician-assisted suicide and euthanasia in the critical care setting. We present areas of common ground and important unresolved differences. CONCLUSIONS: We reached differing positions on the first three core ethical questions and achieved unanimity on how critical care clinicians should manage conscientious objections related to physician-assisted suicide and euthanasia. The alternative positions presented in this article may serve to promote open and informed dialogue within the critical care community.
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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 | 0.074 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.050 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.014 | 0.024 |
| Insufficient payload (model declined to judge) | 0.002 | 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".