Quality of Death
Bibliographic record
Abstract
CONTEXT: The value of good end-of-life (EOL) care could be underestimated if its effects are assessed using the standard metric of quality-adjusted survival, especially if the time horizon is limited to the duration of the EOL care. This issue is particularly problematic in the intensive-care unit (ICU) where death is frequent, care is difficult, and costs are high. OBJECTIVES: The objectives of this study were to test whether people would trade healthy life expectancy for better EOL care, to understand how much life expectancy they would trade relative to domains of good care, and to determine the association of respondent characteristics to time traded. DESIGN AND SUBJECTS: We used a computerized survey instrument describing hypothetical patient experiences in the ICU used to assess attitudes of a general population sample (n = 104) recruited in Pittsburgh, Pennsylvania. MEASURES: We used life expectancy traded (from a baseline of 80 healthy years followed by a 1-month fatal ICU stay) for improving ICU care in 4 domains: pain and discomfort, daily surroundings, treatment decisions, and family support. RESULTS: Three fourths of respondents (n = 78) were prepared to shorten healthy life for better EOL care. Median time traded in individual domains ranged from 7.2 to 7.7 months overall and 9.6 to 11.4 months when restricted to those willing to trade. Median time traded for improvement in all domains was 8.3 months overall and 24.0 months by those willing to trade. In multivariable analyses, respondents who were older, nonwhite, or had children traded significantly less time, whereas those who did not perceive the ICU to be a caring environment traded more time. CONCLUSIONS: Good EOL care is highly valued, both in terms of medical and nonmedical domains, as suggested by previous work and confirmed by our data showing respondents trading quantities of healthy life several times longer than the duration of the EOL period itself. The considerable interperson variation highlights the importance of soliciting individual preferences about EOL care.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 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".