The relationship between depression and physical symptom burden in advanced cancer
Bibliographic record
Abstract
BACKGROUND: Although an association between depression and physical burden has been demonstrated in advanced cancer, it remains unclear to what extent this is limited to specific physical symptoms, such as pain and fatigue, and is mediated by disease and treatment-related factors. We therefore investigated the relationship between depression and physical burden across a multitude of physical symptoms in this population, while controlling for cancer-related factors including disease severity and proximity to death. PATIENTS AND METHODS: A secondary analysis was performed on cross-sectional data in 487 patients with advanced cancer. Measures included the Beck Depression Inventory II and the Memorial Symptom Assessment Scale, which measured physical burden across 24 common cancer symptoms. Disease severity was assessed by survival time and by functional status using the Karnofsky Performance Status scale. RESULTS: Depression severity significantly correlated with number of physical symptoms, symptom distress and symptom severity independent of cancer type, functional status, chemotherapy status and survival time (all p<0.001). Depression was associated with increased incidence, severity and distress across multiple physical symptoms and was an independent predictor of physical burden on multiple regression analysis. CONCLUSIONS: These findings support the view that a synergistic relationship exists between depression and a broad array of physical symptoms 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 | 0.001 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".