Co-expression of ER-beta and HER2 associated with poorer prognosis in primary breast cancer
Bibliographic record
Abstract
PURPOSE: To assess the prognostic value of co-expression of estrogen receptor (ER)-beta and human epidermal growth factor receptor 2 (HER2) in primary breast cancer patients in China. METHODS: Tumour specimens from 308 patients undergoing surgery for primary breast cancer were evaluated. Expression of ER-beta and HER-2 was investigated by the immunohistochemistry. RESULTS: 123 patients (40%) were ER-beta positive and 58 (18.5 %) were HER2 positive. Among the 58 HER2 positive patients, 44 were ER-beta positive and 14 were ER-beta negative. ER-beta positive was associated with HER2 positive (75.9%, P=0.018) as well as ER-alpha positive (79.7%, P=0.023), poor cell differentiation (77.2% grade 2 or 3, P=0.010) and menopause age < 45 yr (55.3%, P=0.031). HER2 positive was associated with poor cell differentiation (93.1%, P=0.001), > or = 3 cm tumour size (67.2%, P=0.011). CONCLUSION: Both ER-beta positive and HER2 positive status was associated with poorer overall survival (OS) by univariate analysis. In both HER2 positive and HER2 negative subgroups, ER-beta positive was associated with poorer distant disease free survival (DDFS) but not OS, which implied that ER-beta might relate to metastasis in breast cancer.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".