Adherence to adjuvant endocrine therapy in estrogen receptor–positive breast cancer patients with regular follow-up
Bibliographic record
Abstract
BACKGROUND: Adjuvant hormonal therapy is crucial in the treatment of estrogen receptor-positive breast cancer. The nonadherence rate to hormonal treatment is reported to be as high as 60%. The goal of this study was to evaluate the factors evoked by the patients as well as the demographic and disease-related factors that could be associated with nonadherence to adjuvant hormonal therapy. METHODS: All consecutive patients treated for an estrogen receptor-positive breast cancer who showed up for regular follow-up with a single breast specialist between November 2008 and April 2009 were included in the study. We assessed adherence to hormonal therapy (either with tamoxifen or aromatase inhibitor). Reasons for adherence and nonadherence were collected. Records were also reviewed for demographic and cancer characteristics and for treatment components. RESULTS: We included 161 patients in the study; 150 (93.2%) adhered to hormonal treatment. Side effects and absence of conviction were the main reasons for nonadherence. The importance of the diagnosis of cancer, fear of recurrence and regular follow-up were reported as the main reasons for adherence. CONCLUSION: Severity of disease and side effects are associated with nonadherence to treatment. Strict follow-up appears to be a necessary adjunct in the adherence to treatment. The association between demographic and cancer characteristics and treatment components needs further investigation. However, these factors may help identify patients at risk of nonadherence and help the oncology team.
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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 | 0.009 |
| 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 | 0.000 | 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".