Hormone Receptor Expression on Endocrine Therapy in Patients with Breast Cancer: A Meta-Analysis
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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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.045 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".