Family Members’ Understanding of the End-of-Life Wishes of People Undergoing Maintenance Dialysis
Bibliographic record
Abstract
Background and objectives People receiving maintenance dialysis must often rely on family members and other close persons to make critical treatment decisions toward the end of life. Contemporary data on family members’ understanding of the end-of-life wishes of members of this population are lacking. Design, setting, participants, & measurements Among 172 family members of people undergoing maintenance dialysis, we ascertained their level of involvement in the patient’s care and prior discussions about care preferences. We also compared patient and family member responses to questions about end-of-life care using percentage agreement and the κ -statistic. Results The mean (SD) age of the 172 enrolled family members was 55 (±17) years, 136 (79%) were women, and 43 (25%) were Black individuals. Sixty-seven (39%) family members were spouses or partners of enrolled patients. A total of 137 (80%) family members had spoken with the patient about whom they would want to make medical decisions, 108 (63%) had spoken with the patient about their treatment preferences, 47 (27%) had spoken with the patient about stopping dialysis, and 56 (33%) had spoken with the patient about hospice. Agreement between patient and family member responses was highest for the question about whether the patient would want cardiopulmonary resuscitation (percentage agreement 83%, κ =0.31), and was substantially lower for questions about a range of other aspects of end-of-life care, including preference for mechanical ventilation (62%, 0.21), values around life prolongation versus comfort (45%, 0.13), preferred place of death (58%, 0.07), preferred decisional role (54%, 0.15), and prognostic expectations (38%, 0.15). Conclusions Most surveyed family members reported they had spoken with the patient about their end-of-life preferences but not about stopping dialysis or hospice. Although family members had a fair understanding of patients’ cardiopulmonary resuscitation preferences, most lacked a detailed understanding of their perspectives on other aspects of end-of-life 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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".