When the decision to die interferes with the duty to heal
Bibliographic record
Abstract
BACKGROUND: This commentary was inspired by an encounter M. M. experienced while shadowing a physician in 2024. The physician referred an otherwise healthy patient between 64 and 74 years old for a routine colonoscopy due to relevant risk factors. However, instead of the anticipated report, they received a letter from the specialist stating their refusal to complete the procedure. The reason cited for refusal: medical assistance in dying (MAiD). In the meeting with the specialist, the patient mentioned that they were considering pursuing MAiD for depression in 2026 - a choice that, notably, would not be available for solely mental health conditions until March 17, 2027. RESULTS/CONCLUSION: Here, we consider multiple angles centred around how we should treat MAiD, particularly when it intersects with decisions related to life expectancy. Policy reform is necessary to address this potential form of discrimination across all subspecialties in medicine, advocating instead for collaborative, case-by-case decision-making between physicians and patients to discuss their goals of care and risks. To this end, we propose a four-pronged approach, including guidelines, medical ethics training, patient-targeted education, and further research.
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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 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.041 | 0.033 |
| Insufficient payload (model declined to judge) | 0.005 | 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".