Re-analysis of symptom clusters in advanced cancer patients attending a palliative outpatient radiotherapy clinic
Bibliographic record
Abstract
BACKGROUND: Cancer patients often present with several concurrent symptoms. There is evidence to suggest that related symptoms can cluster together in stable groups. The present study sought to identify symptom clusters in advanced cancer patients using the Edmonton Symptom Assessment System (ESAS) in a palliative outpatient radiotherapy clinic. METHODS: Principal component analysis (PCA), exploratory factor analysis (EFA), and hierarchical cluster analysis (HCA) were used to identify symptom clusters among the 9 ESAS items using ESAS scores from each patient's first visit. RESULTS: PCA identified three symptom clusters (cluster 1: depression, anxiety; cluster 2: nausea, dyspnea, loss of appetite; cluster 3: pain, well-being, tiredness, drowsiness). EFA identified two clusters (cluster 1: tiredness, drowsiness, loss of appetite, well-being, pain, nausea, dyspnea; cluster 2: depression, anxiety). HCA identified three symptom clusters (cluster 1: depression, anxiety, pain, well-being; cluster 2: tiredness, drowsiness, dyspnea; cluster 3: nausea, loss of appetite). CONCLUSIONS: Symptom clusters were identified using three analytical methods. The following items were always in the same cluster: depression and anxiety; nausea and appetite loss; well-being and pain; tiredness and drowsiness. Further research in symptom clusters is necessary to advance our understanding of the complex symptom interactions in advanced cancer patients and to determine the most clinically relevant symptom clusters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".