Mortality and recurrence rates among systemically untreated high risk breast cancer patients included in the DBCG 77 trials
Bibliographic record
Abstract
BACKGROUND: Following loco-regional treatment for early breast cancer accurate prognostication is essential for communicating benefits of systemic treatment. The aim of this study was to determine time to recurrence and long-term mortality rates in high risk patients according to patient characteristics and subtypes as assigned by immunohistochemistry panels. PATIENTS AND METHODS: In November 1977 through January 1983, 2862 patients with tumors larger than 5 cm or positive axillary nodes were included in the DBCG 77 trials. Archival tumor tissue from patients randomly assigned to no systemic treatment was analyzed for ER, PR, Ki67, EGFR and HER2. Intrinsic subtypes were defined as follows: Luminal A, ER or PR >0%, HER2-negative, PR >10% and Ki67 < 14%; Luminal B, ER or PR >0%, (PR ≤10% or HER2-positive or Ki67 ≥ 14%); HER2E, ER 0%, PR 0%, HER2 positive; Core basal, ER 0%, PR 0%, HER2 negative and EGFR positive. Multivariate categorical and fractional polynomials (MFP) models were used to construct prognostic subsets by clinicopathologic characteristics. RESULTS: In a multivariate model, mortality rate was significantly associated with age, tumor size, nodal status, invasion, histological type and grade, as well as subtype classification. CONCLUSIONS: With 35 years of follow-up, in this population of high-risk patients with no systemic therapy, no subgroup based on a composite prognostic score and/or molecular subtypes could be identified without excess mortality as compared to the background population.
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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 | 0.001 | 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) | 0.000 | 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".