How do general end-of-life treatment goals and values relate to specific treatment preferences? A population-based study
Bibliographic record
Abstract
BACKGROUND: There is a lack of research on the relationship between general end-of-life goals and values and preferences for specific life-sustaining treatments. AIM: To examine agreement between Dutch older people's general end-of-life goals and specific life-sustaining treatment preferences. DESIGN: Participants identified general end-of-life goals in an interview and preferences for four life-sustaining treatments in hypothetical cancer and dementia scenarios in a separate questionnaire. Agreement between general goals and specific treatment preferences was calculated. SETTING/PARTICIPANTS: In total, 1818 older people from 11 representative Dutch municipalities participated in the study. RESULTS: In total, 1168 (response rate 73%) answered questions on general end-of-life and specific treatment preferences. Agreement between a desire to live as long as possible, irrespective of health problems, and a preference for life-sustaining treatments ranged from 51% to 76% in cancer and 41% to 60% in dementia scenarios, depending on the treatment. Agreement between a desire for a shorter life, if without major health problems, and a preference to forgo treatments ranged from 61% to 79% in cancer and 75% to 88% in dementia scenarios. CONCLUSION: For a sizable minority of participants, specific treatment preferences did not agree with their general end-of-life goals. The more frequent desire to forgo treatments in case of dementia than cancer suggests that physical deterioration is more acceptable than cognitive decline. The findings underline the importance of discussing general care goals, different end-of-life scenarios and the risks and burdens of treatments to frame discussions of more specific treatment preferences.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".