Baseline Characteristics, Treatment Patterns, and Outcomes in Patients with HER2-Positive Metastatic Breast Cancer by Hormone Receptor Status from SystHERs
Bibliographic record
Abstract
Abstract Purpose: We report treatments and outcomes in a contemporary patient population with HER2-positive metastatic breast cancer (MBC) by hormone receptor (HR) status from the Systemic Therapies for HER2-positive Metastatic Breast Cancer Study (SystHERs). Experimental Design: SystHERs (NCT01615068) was an observational, prospective registry study of U.S.-based patients with newly diagnosed HER2-positive MBC. Endpoints included treatment patterns and clinical outcomes. Results: Of 977 eligible patients (enrolled from 2012 to 2016), 70.1% (n = 685) had HR-positive and 29.9% (n = 292) had HR-negative disease. Overall, 59.1% (405/685) of patients with HR-positive disease received any first-line endocrine therapy (with or without HER2-targeted therapy or chemotherapy); 34.9% (239/685) received HER2-targeted therapy + chemotherapy + sequential endocrine therapy. Patients with HR-positive versus HR-negative disease had longer median overall survival (OS; 53.0 vs 43.4 months; hazard ratio, 0.70; 95% confidence interval, 0.56–0.87). Compared with patients with high HR-positive staining (10%–100%, n = 550), those with low HR-positive staining (1%–9%, n = 60) received endocrine therapy less commonly (64.2% vs 33.3%) and had shorter median OS (53.8 vs 40.1 months). Similar median OS (43.4 vs 40.1 months) was observed in patients with HR-negative versus low HR-positive tumors (1%–9%). Conclusions: Despite evidence that first-line HER2-targeted therapy, chemotherapy, and sequential endocrine therapy improves survival in patients with HR-positive, HER2-positive disease, only 34.9% of patients in this real-world setting received such treatment. Patients with low tumor HR positivity (1%–9%) had lower endocrine therapy use and worse survival than those with high tumor HR positivity (10%–100%).
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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 | 0.001 | 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) | 0.002 | 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".