Hormone Receptor Expression on Endocrine Therapy in Patients with Breast Cancer: A Meta-Analysis
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Bibliographic record
Abstract
Objective To evaluate the role of hormone receptor expression on endocrine therapy in patients with breast cancer. Methods The databases were used to collect the effect of high expression and low expression of hormone receptors on the efficacy of endocrine therapy in breast cancer. Two evaluators independently screened the literature based on preset inclusion and exclusion criteria. The quality of the article was evaluated using a modified Newcastle-Ottawa Scale (NOS) system. The survival data included in the literature were extracted and the ln(hazard ratio (HR)) and se[ln(HR)] of the overall survival (OS), disease-free survival (DFS), and recurrence-free survival (RFS) rates were calculated according to different level of hormone receptors. The RevMan 5.3 software was used to evaluate the meta-analysis. Results A total of 13 relevant literature were included in the study. There were 8318 estrogen receptor (ER)-positive and 7926 progesterone receptor (PR)-positive patients. Overall survival, DFS, and RFS rates in high expression of ER(+) patients were significantly higher in low expression of ER(+) patients (OS HR = .59, 95% confidence interval (CI): .46-.76, P < .0001; DFS HR = .62, 95%CI: .50-.76, P < .00001; RFS HR = .44, 95% CI: .33-.58, P < .00001). In patients with high expression of PR(+), OS, DFS, and RFS rates were significantly higher than those with low expression of PR(+) (OS HR = .66, 95% CI: .57-.78, P < .00001; DFS HR = .52, 95% CI: .42-.65, P < .00001; RFS HR = .24, 95% CI: .11-.53, P = .0004). Conclusion The expression of ER and PR are powerful predictors of adjuvant endocrine therapy response. Breast cancer patients with high expression of hormone receptors benefit more from endocrine therapy and have better prognosis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.000 |
| 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 it