Using the Edmonton Symptom Assessment System (ESAS) to Describe Symptom Burden Associated with Breast Cancer and Related Treatments: A Cross-Sectional Study
Bibliographic record
Abstract
Background: Symptom burden and functional impairment are common in women with breast cancer, yet their prevalence and clinical significance across the disease spectrum remain underexplored. We sought to describe symptom burden and performance status using patient-reported outcome measures and to identify patient characteristics associated with symptoms requiring clinical intervention. Methods: In this cross-sectional study, women with stage I–IV breast cancer completed the Edmonton Symptom Assessment System (ESAS) and the Patient-Reported Functional Status tool. We assessed the prevalence and severity of symptoms and calculated summary distress scores. Multivariable logistic regression was used to identify patient characteristics associated with clinically significant symptoms (ESAS ≥ 4). Results: Among 381 women (mean age 56.8 years; 27% metastatic; 72% with no comorbidities), 70% reported at least one moderate to severe symptom. The most common were tiredness (31%), lack of well-being (30%), and anxiety (21%). Mean summary distress scores were low overall. Most patients reported functional status scores of 0 or 1, and 43% of those with scores ≥2 had metastatic disease. Compared with metastatic patients, women within the first year after diagnosis were less likely to report a symptom requiring intervention (OR 0.49, 95% CI 0.24–0.90). Conclusions: Clinically significant symptoms are common among women with breast cancer, including those with potentially curable disease. Threshold-based use of ESAS, rather than reliance on mean scores, provides a more accurate assessment of patient needs. These findings support the routine integration of patient-reported outcomes into oncology care and underscore the importance of targeted multidisciplinary interventions.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".