Improved Survival of HER2-Positive Metastatic Breast Cancer in Routine Care
Bibliographic record
Abstract
Purpose: Targeted therapy directed against HER2 has improved the outcome of patients with HER2-positive metastatic breast cancer in prospective clinical trials. No data are available how anti-HER2-therapy has influenced survival of unselected patients with HER2-positive metastatic breast cancer who receive treatment in routine care. Methods: Data of 118 subsequent patients with HER2-positive metastatic breast cancer who were treated in a community-based oncology group practice between 1995 and 2010 were analyzed retrospectively. Results: Median age at initial diagnosis of metastasis was 58 years (33-92). Twenty-two percent were metastasized at diagnosis of breast cancer, seventy-eight percent developed metastases in the course of the disease. Distribution of metastatic sites at initial diagnosis of metastasis was: 23% bone, 58% visceral, 5% CNS, 10% lymph nodes, 4% other. Hormone receptor was positive in 68% and negative in 31%. Palliative treatment consisted of antihormonal therapy in 56%, chemotherapy in 90% and radiotherapy in 50%. Seventy-five percent received anti-HER2-therapy consisting of trastuzumab in 80% and lapatinib in 1%, 19% received trastuzumab + lapatinib sequentially. Twenty-five percent received no anti-HER2-therapy. Median overall survival since initial diagnosis of metastasis was 34 months (0-277+). Conclusion: It could be shown that, compared to historical controls, targeted therapy against HER2 prolongs overall survival of patients with HER2-positive metastatic breast cancer who receive treatment in routine care.
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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 | 0.002 |
| 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) | 0.001 | 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".