Patient-reported symptoms before adjuvant locoregional radiotherapy for breast cancer: triple-negative histology impacts the symptom burden
Bibliographic record
Abstract
BACKGROUND: Multimodal breast cancer treatment may cause side effects reflected in patient-reported outcomes and/or symptom scores at the time of treatment planning for adjuvant radiotherapy. In our department, all patients have been assessed with the Edmonton Symptom Assessment System (ESAS; a questionnaire addressing 11 major symptoms and wellbeing on a numeric scale of 0-10) at the time of treatment planning since 2016. In this study, we analyzed ESAS symptom severity before locoregional radiotherapy. PATIENTS AND METHODS: Retrospective review of 132 patients treated between 2016 and 2021 (all comers in breast-conserving or post-mastectomy settings, different radiotherapy fractionations) was performed. All ESAS items and the ESAS point sum were analyzed to identify subgroups with higher symptom burden and thus need for additional care measures. RESULTS: The biggest patient-reported issues were fatigue, pain, and sleep problems. Patients with triple negative breast cancer reported a higher symptom burden (mean 30 versus 20, p = 0.038). Patients assigned to adjuvant endocrine therapy had the lowest point sum (mean 18), followed by those on Her-2-targeting agents without chemotherapy (mean 19), those on chemotherapy with or without other drugs (mean 26), and those without systemic therapy (mean 41), p = 0.007. Those with pathologic complete response after neoadjuvant treatment had significantly lower anxiety scores (mean 0.7 versus 1.8, p = 0.03) and a trend towards lower depression scores, p = 0.09. CONCLUSION: Different surgical strategies, age, and body mass index did not impact on ESAS scores, while the type of adjuvant systemic therapy did. The effect of previous neoadjuvant treatment and unfavorable tumor biology (triple negative) emerged as important factors associated with symptom burden, albeit in different domains. ESAS data may facilitate identification of patients who should be considered for additional supportive measures to alleviate specific symptoms.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".