End‐of‐Life Decisions in Individuals Dying with Dementia in Belgium
Bibliographic record
Abstract
OBJECTIVES: To describe the prevalence and characteristics of end-of-life decisions in individuals dying with dementia in Belgium. DESIGN: Retrospective mail survey. SETTING: Brussels and Flanders, Belgium. PARTICIPANTS: Physicians certifying a large random sample of deaths (n = 8,627). MEASUREMENTS: End-of-life decision-making in individuals dying with dementia (n = 361) was compared through bivariate and multivariable analyses with decision-making in individuals dying from cancer (n = 1,276) as reference group. RESULTS: Response rate was 54.7%. The intense pain and symptoms of individuals with dementia was alleviated less often than those of individuals with cancer (odds ratio (OR) = 0.50, P < .001), and individuals with dementia were more likely to have life-prolonging treatment withheld or withdrawn (OR = 1.40, P = .048). Five individuals with dementia (1.3%) requested euthanasia but did not receive it; 60 (4.7%) individuals with cancer who requested euthanasia received it, and 48 (3.8%) who requested it did not (OR = 0.04, P = .047). Individuals with dementia rarely had capacity to participate in decision-making, which more often involved their families than in individuals with cancer (OR = 1.99, P = .009). Little prospect of improvement (59.7%), pointless life prolongation (54.5%), poor quality of life (46.2%), and prevention of further suffering (37.6%) were important reasons for end-of-life decisions in individuals with dementia. CONCLUSION: End-of-life decision-making in individuals dying with dementia differs from that in individuals with cancer, more often involving forgoing of life-prolonging treatment and less often involving intensifying pain and symptom treatment. Considerations typically involve the prospects of the individual with dementia as opposed to the severity of their present situation. Optimal processes of advance care planning may address the burdensome decision falling to physicians and family regarding when to allow the individual to die.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".