Early palliative care and quality of dying and death in patients with advanced cancer
Bibliographic record
Abstract
OBJECTIVE: Early palliative care (EPC) in the outpatient setting improves quality of life for patients with advanced cancer, but its impact on quality of dying and death (QODD) and on quality of life at the end of life (QOL-EOL) has not been examined. Our study investigated the impact of EPC on patients' QODD and QOL-EOL and the moderating role of receiving inpatient or home palliative care. METHOD: Bereaved family caregivers who had provided care for patients participating in a cluster-randomised trial of EPC completed a validated QODD scale and indicated whether patients had received additional home palliative care or care in an inpatient palliative care unit (PCU). We examined the effects of EPC, inpatient or home palliative care, and their interactions on the QODD total score and on QOL-EOL (last 7 days of life). RESULTS: A total of 157 caregivers participated. Receipt of EPC showed no association with QODD total score. However, when additional palliative care was included in the model, intervention patients demonstrated better QOL-EOL than controls (p=0.02). Further, the intervention by PCU interaction was significant (p=0.02): those receiving both EPC and palliative care in a PCU had better QOL-EOL than those receiving only palliative care in a PCU (mean difference=27.10, p=0.002) or only EPC (mean difference=20.59, p=0.02). CONCLUSION: Although there was no association with QODD, EPC was associated with improved QOL-EOL, particularly for those who also received inpatient care in a PCU. This suggests a long-term benefit from early interdisciplinary palliative care on care throughout the illness. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov Registry (#NCT01248624).
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".