Symptom Reporting Behaviors, Symptom Burden, and Quality of Life in Patients with Hormone Receptor–Positive Breast Cancer Undergoing Adjuvant Endocrine Therapy
Bibliographic record
Abstract
Background: Adjuvant endocrine therapy (AET) enhances survival outcomes in hormone receptor–positive (HR+) breast cancer. However, this treatment is associated with toxicities that may adversely affect the quality of life (QoL) and impact patient–physician communication. A thorough understanding of symptom-reporting behaviors is essential for optimizing survivorship care. Methods: This cross-sectional study surveyed 191 female patients with HR+ breast cancer undergoing adjuvant AET (tamoxifen or aromatase inhibitors ± ovarian function suppression [OFS]) at Antalya Training and Research Hospital between July and August 2025. QoL, symptom burden, and adverse event (AE) reporting behaviors were assessed using validated instruments (European Organization for Research and Treatment of Cancer Quality of Life Questionnaire C30 [EORTC QLQ-C30], adapted Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events [PRO-CTCAE]). Categorical variables were compared using chi-square tests, and multivariate analyses were performed using logistic regression. Results: The median age was 54 years (interquartile range [IQR]: 46–61 years). The following independent variables were identified as predictors of a higher symptom burden: prior chemotherapy (odds ratio [OR]: 3.75; 95% confidence interval [CI]: 1.46–9.69; p = 0.006), OFS use (OR: 3.29; 95% CI: 1.51–7.15; p = 0.003), AE reporting to physicians (OR: 3.52; 95% CI: 1.80–6.88; p < 0.001), and complementary and alternative medicine (CAM) use (OR: 7.27; 95% CI: 1.57–33.63; p = 0.011). Independent predictors of poor QoL included receiving psychological support (OR: 0.36; 95% CI: 0.19–0.67; p = 0.002) and AE reporting (OR: 0.28; 95% CI: 0.13–0.64; p = 0.001). Conclusions: Symptom burden and QoL in patients with HR+ breast cancer receiving AET are influenced by clinical history, including chemotherapy and OFS; behavioral factors, such as reporting behaviors; and supportive care, including CAM and psychological support. The routine integration of patient-reported outcomes and proactive symptom monitoring is crucial for delivering personalized and effective survivorship care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".