Comparison of Breast Cancer Recurrence and Outcome Patterns Between Patients Treated From 1986 to 1992 and From 2004 to 2008
Bibliographic record
Abstract
PURPOSE: To determine whether the patterns of relapse according to estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) status changed in the contemporary era. PATIENTS AND METHODS: Female patients referred to the British Columbia Cancer Agency with biopsy-proven stage I to III breast cancer (BC), diagnosed between 1986 and 1992 (cohort 1 [C1]) and between mid-2004 and 2008 (cohort 2 [C2]), and with known ER and HER2 status were eligible. Data were prospectively collected. C2 patients were matched to C1 patients for stage, grade, and ER and HER2 status. The primary end point was hazard rate of relapse (HRR) for BC by study cohort according to biomarker status. Secondary outcomes included HRR according to stage, grade, and age and hazard rate of death (HRD). RESULTS: After matching, 7,178 patients were included (3,589 patients in each cohort). BC subtype distribution was as following ER positive/HER2 negative, 70.8%; ER positive/HER2 positive, 6.9%; ER negative/HER2 positive, 6.6%; and ER negative/HER2 negative, 15.8%. For the overall population, the HRR approximately halved in all yearly intervals to year 9 in C2 compared with C1. Differences in HRR between cohorts were greater in the initial five intervals for HER2-positive and ER-negative/HER2-negative BC. The HRR decreased in C2 compared with C1 for all disease stages and grades. The HRD in C2 also decreased compared with C1, although to a lesser extent. CONCLUSION: Although the pattern of relapse remains similar, there has been a significant improvement in BC relapse-free survival. Outcomes have improved for all BC subtypes, especially HER2-positive and ER-negative/HER2-negative BC, with the early spike in disease recurrence markedly decreased. These contemporary hazard rates are important for treatment decisions, patient discussions, and planning clinical trials of early BC.
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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 | 0.001 |
| 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".