Adherence to Adjuvant Endocrine Therapy in Breast Cancer Patients
Bibliographic record
Abstract
BACKGROUND: Adjuvant endocrine therapy (AET) reduces breast cancer recurrence and mortality of women with hormone-receptor-positive tumors, but poor adherence remains a significant problem. The aim of this study was to analyze AET side effects and their impact on adherence to treatment. METHODS: A total of 373 breast cancer patients treated with AET filled out a specific questionnaire during their follow up visits at the Breast Unit of our Centre. RESULTS: Side effects were reported by 81% of patients, 84% of those taking tamoxifen and 80% of those taking aromatase inhibitors (AIs). The most common side effect in the tamoxifen group was hot flashes (55.6%), while in the AI group it was arthralgia (60.6%). The addition of GnRH agonists to both tamoxifen and AI significantly worsened all menopausal symptoms. Overall, 12% of patients definitively discontinued AET due to side effects, 6.4% during the first 5 years and 24% during extended therapy. Patients who had previously received chemotherapy or radiotherapy reported a significantly lower discontinuation rate. CONCLUSIONS: AET side effects represent a significant problem in breast cancer survivors leading to irregular assumption and discontinuation of therapy. Adherence to AET may be improved by trustful patient-physician communication and a good-quality care network.
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 | 0.006 |
| 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 | 0.000 | 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".