To die, to sleep, perchance to dream? A response to DeMichelis, Shaul and Rapoport
Bibliographic record
Abstract
In developing their policy on paediatric medical assistance in dying (MAID), DeMichelis, Shaul and Rapoport decide to treat euthanasia and physician-assisted suicide as ethically and practically equivalent to other end-of-life interventions, particularly palliative sedation and withdrawal of care (WOC). We highlight several flaws in the authors' reasoning. Their argument depends on too cursory a dismissal of intention, which remains fundamental to medical ethics and law. Furthermore, they have not fairly presented the ethical analyses justifying other end-of-life decisions, analyses and decisions that were generally accepted long before MAID was legal or considered ethical. Forgetting or misunderstanding the analyses would naturally lead one to think MAID and other end-of-life decisions are morally equivalent. Yet as we recall these well-developed analyses, it becomes clear that approving of some forms of sedation and WOC does not commit one to MAID. Paediatric patients and their families can rationally and coherently reject MAID while choosing palliative care and WOC. Finally, the authors do not substantiate their claim that MAID is like palliative care in that it alleviates suffering. It is thus unreasonable to use this supposition as a warrant for their proposed policy.
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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 | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.069 | 0.076 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".