Predictors of fatigue and quality of life in a prospective palliative care cohort
Bibliographic record
Abstract
8571 Background: Palliative care seeks to minimize distress at the end of life. Fatigue significantly diminishes quality of life (QOL) in this population. Are there potentially modifiable factors that influence fatigue and QOL? Methods: This analysis focuses on a subset of 198 patients from a larger 2×2×2 factorial randomized trial of pain education and care coordination conducted in South Australia. Selected participants were adults referred to a community palliative care service with pain in the preceding 3 months and a hemoglobin assessment within 14 days of enrollment. Pain, other symptoms, and Australia-modified Karnofsky Performance Status (AKPS) were recorded at enrollment. Predictors considered were anxiety, depression, dyspnea, constipation, pain, AKPS, hemoglobin, age, and gender. Dependent variables were global QOL from the McGill QOL Questionnaire and fatigue. Using forward stepwise linear regression, multivariate models predicting fatigue and QOL were constructed from significant univariate variables. Results: Mean age was 69 (standard deviation (SD) 13); 97% had cancer. Most frequent diagnoses were lung (18%), hematological (15%), and colorectal (15%) malignancies. Mean hemoglobin was 11.4 gm/dL (SD 1.9); median AKPS 60%; mean worst pain 4.0 (SD 3.4; 0–10 scale). Distressing symptoms (3–4 on 0–4 scales) included dyspnea (22%), constipation (13%), anxiety (11%), and depression (6%). Mean QOL was 5.9 (SD 2.0) on a 0–10 scale; mean fatigue was 2.3 (SD 1.0) on a 0–4 scale. The final multivariate model predicting fatigue included AKPS (p<0.01), constipation (p=0.02), and dyspnea (p=0.06). Hemoglobin was not predictive of fatigue (univariate p=0.7069). QOL was significantly influenced by fatigue (p=0.03), anxiety (p< 0.01), and AKPS (p= 0.01). Conclusions: Fatigue was driven by performance status, constipation, and dyspnea. In contrast to an oncology population, hemoglobin was not a significant contributor to fatigue in this population, consistent with other palliative care cohorts. QOL was driven by fatigue, anxiety, and performance status. This analysis of a prospectively collected population suggests that performance status, constipation, dyspnea, and anxiety are potentially modifiable variables impacting fatigue and QOL in the palliative care setting. No significant financial relationships to disclose.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".