To live and die well with neurocognitive disorders
Bibliographic record
Abstract
The end of life in Major Neurocognitive Disorder (MND) is often shaped by a range of medical complications. These can include aspiration pneumonia, hip fractures, subdural hemorrhages, untreated cancer, or pyelonephritis. Regardless of the cause, the provision of appropriate, compassionate care remains a critical element. In Quebec, the recent legalization of medical assistance in dying (MAiD) through advance requests for individuals with MND introduced new dimensions to how these individuals approach death. What are the ethical implications of this change? In this presentation, we aim to explore how death occurs from MND, specifically in the context of Quebec in 2025, and the ethical dilemmas associated with this shift. To understand these ethical concerns, it is also crucial to examine what constitutes a "good death" in MND. A "good death" is not only about the medical management of end-of-life symptoms but also about the opportunity to maintain dignity, receive emotional support, and, for some, exercise personal autonomy in decision-making. We will discuss these concepts and present an article we have written and published in the scientific journal Mortality. This article discusses the difficult choices faced by individuals with MND: whether to die quickly and painlessly or to take time to say goodbye to loved ones-a decision that is often fraught with emotional and ethical challenges. These issues were raised during semi-structured interviews conducted by Dr. Pageau, who gathered data as part of a research project on the determination of care levels in geriatric patients in Quebec. The interviews provided valuable insights into the personal, social, and ethical dimensions of end-of-life decision-making for people with MND. We will present the results of this study to help foster an ethical discussion about the complex issues surrounding care and the dying process for individuals with neurocognitive disorders.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".