Recognizing difficult trade-offs: values and treatment preferences for end-of-life care in a multi-site survey of adult patients in family practices
Bibliographic record
Abstract
BACKGROUND: Decisions about care options and the use of life-sustaining treatments should be informed by a person's values and treatment preferences. The objective of this study was to examine the consistency of ratings of the importance of the values statements and the association between values statement ratings and the patient's expressed treatment preference. METHODS: We conducted a multi-site survey in 20 family practices. Patients aged 50 and older self-completed a questionnaire assessing the importance of eight values (rated 1 to 10), and indicated their preference for use of life-sustaining treatment (5 options). We compared correlations among values to a priori hypotheses based on whether the value related to prolonging or shortening life, and examined expected relationships between importance of values and the preference option for life-sustaining treatment. RESULTS: Eight hundred ten patients participated (92% response rate). Of 24 a priori predicted correlations among values statements, 14 were statistically significant but nearly all were negligible in their magnitude and some were in the opposite direction than expected. For example, the correlation between importance of being comfortable and suffering as little as possible and the importance of living as long as possible should have been inversely correlated but was positively correlated (r = 0.08, p = 0.03). Correlations between importance of values items and preference were negligible, ranging from 0.03 to 0.13. CONCLUSIONS: Patients may not recognize that trade-offs in what is most important may be needed when considering the use of treatments. In the context of preparation for decision-making during serious illness, decision aids that highlight these trade-offs and connect values to preferences more directly may be more helpful than those that do not.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".