Physician-Assisted Suicide and Euthanasia
Bibliographic record
Abstract
Medical professional societies have traditionally opposed physician-assisted suicide and euthanasia (PAS-E), but this opposition may be shifting. We present 5 reasons why physicians shouldn't be involved in PAS-E. 1. Slippery slopes: There is evidence that safeguards in the Netherlands and Belgium are ineffective and violated, including administering lethal drugs without patient consent, absence of terminal illness, untreated psychiatric diagnoses, and nonreporting; 2. Lack of self-determination: Psychological and social motives characterize requests for PAS-E more than physical symptoms or rational choices; many requests disappear with improved symptom control and psychological support; 3. Inadequate palliative care: Better palliative care makes most patients physically comfortable. Many individuals requesting PAS-E don't want to die but to escape their suffering. Adequate treatment for depression and pain decreases the desire for death; 4. Medical professionalism: PAS-E transgresses the inviolable rule that physicians heal and palliate suffering but never intentionally inflict death; 5. Differences between means and ends: Proeuthanasia advocates look to the ends (the patient's death) and say the ends justify the means; opponents disagree and believe that killing patients to relieve suffering is different from allowing natural death and is not acceptable. CONCLUSIONS: Physicians have a duty to eliminate pain and suffering, not the person with the pain and suffering. Solutions for suffering lie in improving palliative care and social conditions and addressing the reasons for PAS-E requests. They should not include changing medical practice to allow PAS-E.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".