Adherence to endocrine therapy among Chinese patients with breast cancer: current status and recommendations for improvement
Bibliographic record
Abstract
BACKGROUND: Postoperative endocrine therapy is known to reduce recurrence and mortality in patients with estrogen receptor (ER)- or progestogen receptor (PR)-positive breast cancer. Correlates and determinants of compliance with endocrine therapy among Chinese patients with breast cancer are not known. The aim of this study was to elucidate the efficacy and adherence of endocrine therapy in China and suggest effective improvements on the adherence. PATIENTS AND METHODS: We analyzed the survival of 1,110 patients eligible for endocrine therapy and adherence of 699 patients to endocrine therapy. Kaplan-Meier curves, log-rank tests and Cox proportional hazard models were used to evaluate survival, and logistic regression models were used to assess variables associated with treatment adherence. RESULTS: =0.013). Adherence to endocrine therapy was only 63.1%. Sociodemographic characteristics of patients, clinical- and medication-related characteristics and patients' attitudes were associated with adherence to endocrine therapy. CONCLUSION: Adherence to endocrine therapy in Chinese patients with ER+/PR+ breast cancer was <65%. Both patients and physicians should take progressive steps to improve the rate of adherence.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".