Symptom Clusters Across The Trajectory Of Radiation Therapy In Patients With Breast Cancer
Bibliographic record
Abstract
IntroductionSymptoms experienced by breast cancer patients often cluster together in groups known as u201csymptom clustersu201d. The purpose of this analysis is to determine the symptom clusters before, during, and after radiotherapy (RT) in women with breast cancer. MethodsAll breast cancer patients receiving RT completed the Edmonton Symptom Assessment Scale (ESAS) before, during, and after RT. Exploratory factor analysis (EFA), principal component analysis (PCA), and hierarchical cluster analysis (HCA) were used to identify symptom clusters among the nine ESAS items at all three time points (Tables 1, 2). ResultsA total of 1224 patients were included in this study. The PCA and EFA identified the same two symptom clusters before the start of RT: 1) pain, tiredness, nausea, drowsiness, appetite, and dyspnea; 2) depression, anxiety, and wellbeing (Table 1). The HCA further split the symptoms into three clusters (Table 2). Wellbeing, depression, and anxiety consistently clustered together. Among the ESAS scores collected during and after RT, each statistical method identified different symptom clusters. For the symptom clusters experienced during RT, the following symptoms were always in the same cluster: wellbeing, depression, and anxiety; nausea and appetite; drowsiness and dyspnea. Following RT, depression and anxiety consistently clustered together, with nausea and appetite in the other cluster. ConclusionsAmong the symptom clusters derived before, during, and after RT, the following symptoms consistently presented together: depression and anxiety, nausea and appetite, pain and tiredness, and drowsiness, dyspnea, and tiredness (Table 3). Well-defined symptom clusters in this population can improve management of symptoms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".