Dying Well After Discontinuing the Life-Support Treatment of Dialysis
Bibliographic record
Abstract
BACKGROUND: Cessation of life-prolonging treatments precedes death in an increasing number of cases, but little attention has been accorded to the quality of dying. OBJECTIVE: To examine the quality of dying following dialysis termination. PATIENTS AND METHODS: A prospective cohort, observational study involved 6 dialysis clinics in the United States and 2 clinics in Canada, and 131 adult patients receiving maintenance dialysis who died after treatment cessation. Sixty percent (n = 79) underwent patient (n = 23) and/or family (n = 76) interviews and follow-up with caretakers. A quality of dying tool quantified duration, pain and suffering, and psychosocial factors. RESULTS: The sample was 59% female, the age was 70.0+/-1.2 years old, the duration of dialysis was 34.0+/-2.8 months, and death occurred 8.2+/-0.7 days after the last dialysis treatment. (Data are given as mean +/- SE.) Thirty-eight percent of the subjects who completed the protocol were judged to have had very good deaths, 47% had good deaths, and 15% had bad deaths. During the last day of life, 81% of the sample did not suffer, although 42% had some pain and an additional 5% had severe pain. According to the psychosocial domain of the quality of dying measure, patients who died at home or with hospice care had better deaths than those who died in a hospital or nursing home. CONCLUSIONS: Most deaths following withdrawal of dialysis were good or very good. The influence of site of death and physician attitudes about decisions to stop life support deserves more research attention. Quality of dying tools can be used to establish benchmarks for the provision of terminal care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".