Neuro-Oncology Clinicians’ Attitudes and Perspectives on Medical Assistance in Dying
Bibliographic record
Abstract
BACKGROUND: Medical assistance in dying (MAiD), also known as physician-assisted death, is currently legal in several locations across the globe. Brain cancer or its treatments can lead to cognitive impairment, which can impact decision-making capacity for MAiD. OBJECTIVE: We sought to explore neuro-oncology clinicians' attitudes and perspectives on MAiD, including interpretation of decision-making capacity for patient MAiD eligibility. METHODS: An online survey was distributed to members of national and international neuro-oncology societies. We asked questions about decision-making capacity and MAiD, in part using hypothetical patient scenarios. Multiple choice and free-text responses were captured. RESULTS: There were 125 survey respondents. Impaired cognition was identified as the most important factor that would signal a decline in patient capacity. At least 26% of survey respondents had moral objections to MAiD. Respondents thought that different hypothetical patients had capacity to make a decision about MAiD (range 18%-58%). In other hypothetical scenarios, fewer clinicians were willing to support a MAiD decision for a patient with an oligodendroglioma (26%) vs. glioblastoma (41%-70%, depending on the scenario). Time since diagnosis, performance status, and patient age seemed to affect support for MAiD decisions (Fisher's exact P-values 0.007, < 0.001, and 0.049, respectively). CONCLUSION: While there are differing opinions on the moral permissibility of MAiD in general and for neuro-oncology patients, most clinicians agree that capacity must be assessed carefully before a decision is made. End-of-life discussions should happen early, before the capacity is lost. Our results can inform assessments of patient capacity in jurisdictions where MAiD is legal.
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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 | 0.016 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".