Human Epidermal Growth Factor Receptor 2 Overexpression As a Prognostic Factor in a Large Tissue Microarray Series of Node-Negative Breast Cancers
Bibliographic record
Abstract
PURPOSE: Human epidermal growth factor receptor 2 gene (HER2) is associated with a poorer outcome in node-positive breast cancer, but the results are conflicting in node-negative disease. This study assessed the prognostic impact of HER2 overexpression/amplification in a large series of node-negative breast cancers. PATIENTS AND METHODS: A tissue microarray (TMA) series was constructed consisting of 4,444 invasive breast cancers diagnosed in British Columbia from 1986 to 1992. Within this series, 2,026 patients were node negative, of whom 70% did not receive adjuvant systemic therapy. The TMA series was assessed for estrogen receptor (ER) and HER2. Logistic regression modeling was used to estimate odds ratios at the 10-year follow-up. RESULTS: HER2 was positive in 10.2% of the node-negative cohort. In this cohort, an inferior outcome was seen in patients with HER2-positive tumors compared with HER2-negative tumors for 10-year relapse-free survival (RFS; 65.9% v 75.5%, respectively; P = .01), distant RFS (71.2% v 81.8%, respectively; P = .004), and breast cancer-specific survival (BCSS; 75.5% v 86.3%, respectively; P = .001). A trend for a worse overall survival was also seen (P = .06). HER2 was an independent poor prognostic factor for RFS and BCSS at 10 years, with odds ratios of 1.71 (P = .01) and 2.03 (P = .003), respectively. The number of HER2-positive tumors that were
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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 | 0.000 |
| 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".