Psychological distress and physical symptoms in advanced cancer: cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: Patients with advanced cancer experience varying physical and psychological symptoms throughout the course of their illness. Depression, anxiety and stress affect overall well-being. This study investigates the correlation between emotional distress and physical symptoms in a cohort of patients with advanced cancer. METHODS: There were 238 patients included in this study. Data from participants in two medicinal cannabis randomised controlled trials were analysed. Patients were aged over 18 years and had advanced cancer. Edmonton Symptom Assessment System, and Depression, Anxiety and Stress Scale (DASS-21) were assessed for all patients at baseline. RESULTS: Moderate-severe depression was reported in 29.8% and moderate-severe anxiety was reported in 47.9% of patients. The emotional subscales of DASS-21 (depression, anxiety, stress) correlated with total symptom distress score (p<0.001) and overall well-being (p<0.001). Depression was correlated with physical symptoms of fatigue, nausea, poor appetite and dyspnoea. Anxiety was correlated with fatigue and dyspnoea. Stress was correlated with fatigue, nausea and dyspnoea. CONCLUSIONS: Depression, anxiety and stress were common in this population. The relationship between physical and psychological well-being is complex. A holistic approach to symptom management is required to improve quality of life in patients with advanced cancer.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".