Peaceful Awareness in Patients with Advanced Cancer
Bibliographic record
Abstract
BACKGROUND: Previous studies have shown that prognostic awareness may be harmful to mental health yet beneficial for end of life care planning. The effects of prognostic awareness coupled with a sense of inner peace are unknown. METHODS: In the multisite, longitudinal Coping with Cancer Study, 280 patients with advanced cancer were interviewed at baseline. Patients defining themselves as "terminally ill" and/or "at peace" most days were paired with others on sociodemographic, mental health and advance care planning. Primary caregivers of deceased patients were interviewed 6 months postloss and compared on their physical and mental health and their perceptions of patients' end-of-life care and death. RESULTS: Overall, 17.5% of patients reported being both peaceful and aware. Peacefully aware patients had lower rates of psychological distress and higher rates of advance care planning (e.g., completing do-not-resuscitate [DNR] orders, advance care planning discussions with physicians) than those who were not peacefully aware. Additionally, peacefully aware patients had the highest overall quality of death as reported by their caretakers in a postmortem evaluation. Surviving caregivers of peacefully aware patients were more physically and mentally healthy 6 months postloss than caregivers of patients who were "aware" but not peaceful. CONCLUSIONS: Patients with advanced cancer who are peacefully aware have better mental health and quality of death outcomes, and their surviving caregivers have better bereavement outcomes. Peaceful awareness is associated with modifiable aspects of medical care (e.g., discussions about terminal treatment preferences).
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".