Quality‐of‐Life Trajectories at the End of Life: Assessments over Time by Patients with and without Cancer
Bibliographic record
Abstract
OBJECTIVES: To compare quality-of-life (QOL) ratings of terminally ill patients with and without cancer over time. DESIGN: Secondary analysis of prospective data from a randomized clinical trial. SETTING: Trial conducted with terminally ill patients in Seattle, Washington, testing the efficacy of massage and guided meditation in improving patients' QOL. PARTICIPANTS: One hundred sixty-seven trial participants, of whom 127 provided follow-up data and died before data analysis. MEASUREMENTS: At enrollment, participants reported demographic characteristics, symptom distress, QOL, and primary life-limiting diagnosis. At enrollment and at follow-up interviews after every two study-provided treatment sessions, participants rated their perceived quality of life on a scale from 0 (no quality of life) to 10 (perfect quality). At the end of the study, the investigators added measures of patient's survival status, number of days between study enrollment and death, and receipt of hospice services to the data set. RESULTS: Multilevel models showed significantly steeper QOL declines for patients with cancer than for those without after adjustment for time between study enrollment and death. Over a 4-month before-death period, the average patient without cancer was estimated to experience a QOL decline of approximately 0.6 on a scale from 0 to 10, compared with a 1.2-point decline for patients with cancer. CONCLUSION: Patients with cancer face more-precipitous end-of-life challenges to quality of life than do other terminally ill persons. Therefore, clinicians must address QOL issues-not just symptom burden and distress. By introducing and discussing expected QOL declines at the end of life, clinicians may help to prepare, support, and reassure patients and their families.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".