Association between Molecular Subtypes and Survival in Patients with Breast Cancer
Bibliographic record
Abstract
Background: Aim of this study is to classify intrinsic subtypes and evaluate the differences in clinical/pathological characteristics and survival outcomes among the molecular types. Patients and Methods: Breast cancer subtypes were classified according to the 2013 St. Gallen Consensus. Five molecular subtypes were determined, Luminal A, Luminal B-like HER2 negative, Luminal B-like HER2 positive, HER2 positive, and triple negative. Data was obtained from the records of patients with invasive breast cancer retrospectively. The differences in clinical/pathological parameters, overall survival and disease-free survival among the molecular subtypes were analyzed. The Kaplan-Meier method, log-rank test and Cox regression tests were used to compare groups. Results: The median follow-up period is 48 months. The Luminal B-HER2 negative was the most prevalent type (26.6%). Patient demographics, tumor characteristics and survival data were analyzed. The Luminal A and Luminal B-HER2 negative subtypes had significantly higher overall survival and disease-free survival rates. Multivariate Cox analysis revealed that tumor stage, more than 3 positive axillary lymph node involvement, and breast cancer subtype as significant factors for overall survival and disease-free survival (p<0.05). Triple Negative subtype had a higher relative hazard of local recurrence and distant metastasis (HR=2.69, 95% CI=1.47; 4.95). Conclusions: Breast cancer subtype has significant impact on overall survival and disease-free survival rates. While Luminal A and luminal B HER2 negative subtypes have better outcome, triple negative and HER2- subtypes remain poor.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".