Ten-Year Outcomes in a Population-Based Cohort of Node-Negative, Lymphatic, and Vascular Invasion–Negative Early Breast Cancers Without Adjuvant Systemic Therapies
Bibliographic record
Abstract
PURPOSE: To discuss the absolute benefits from adjuvant systemic therapy knowledge of long-term outcomes and baseline risks of relapse and disease-specific survival are required. We assessed the 10-year outcomes in a population-based cohort of node-negative (N-) lymphovascular negative (LV-) early breast cancers diagnosed from 1989 to 1991 who did not receive adjuvant systemic therapy. METHODS: One thousand one hundred eighty-seven cases of pT(1-2)N(0) LV- breast cancers with a median follow-up of 10.4 years were reviewed. Kaplan-Meier survival curves for relapse free survival (RFS), breast cancer-specific survival (BCSS) and overall survival (OS) were compared with log-rank tests with cohorts stratified for tumor size and grade. RESULTS: The median age of this series was 62 years. Four hundred thirty tumors were < or = 1 cm in diameter (cohort 1), 507 were 1.1-2 cm (cohort 2), and 250 were 2.1 to 5 cm in diameter (cohort 3). The 10-year outcomes for cohorts 1, 2, and 3, respectively, were significantly different: RFS, 82%, 75%, and 66%; BCSS, 92%, 90%, and 77%; and OS, 79%, 78%, and 66%. Tumor grade significantly altered outcome within size cohorts, particularly in pT(1)N(0) breast cancers. CONCLUSION: This study provides detailed information on the continued relapse and breast cancer death rate to 10 years of follow-up. Specifically, without adjuvant systemic therapy, patients with LV-, N - breast cancer had a > or = 25% 10-year risk of relapse and a corresponding 10-year breast cancer death rate of > or = 10% if they had either a grade 3 tumor < or = 1 cm, a grade 2 to 3 tumor from 1.1 to 2 cm, or any grade tumor greater than 2 cm.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".