Risk of early recurrence among postmenopausal women with estrogen receptor‐positive early breast cancer treated with adjuvant tamoxifen
Bibliographic record
Abstract
BACKGROUND: Adjuvant aromatase inhibitors (AIs), instead of or after tamoxifen, are effective in decreasing recurrence in postmenopausal women with estrogen receptor (ER)-positive breast cancer. An understanding of which patients are at risk of early recurrence while they are receiving tamoxifen may improve clinical decision making. METHODS: The patients who were included in this study were women aged >or= 50 years with early-stage, ER-positive breast cancer diagnosed between 1986 and 1999 and had been treated with tamoxifen. Characteristics of the patients with early recurrences (within 2.5 years of diagnosis), late recurrences (between 2.5 years and 5 years) and no recurrence within 5 years were compared. Logistic regression analyses were conducted to identify which groups were at risk of early recurrence. RESULTS: Among 3844 women, 304 women (7.9%) developed disease recurrence within 2.5 years. Higher than average rates of recurrence within 2.5 years were observed in cohorts with lymph node (N)-positive tumors (11.5%), grade 3 histology (14.3%), or low-positive ER levels, ie, 10-49 fmol/mg or 10%-20% staining (14.9%). In multivariate analyses, only pathologically N-positive tumors (1-3 vs 0 positive lymph nodes: odds ratio [OR], 1.6; 4-9 vs 0 positive lymph nodes: OR, 2.23 [P= .03]) and low-positive ER status (OR, 2.04; P= .01) were associated with recurrence within 2.5 years compared with recurrence between 2.5 years and 5 years. Other clinical and pathologic variables were not predictive of early recurrence. CONCLUSIONS: Subgroups of women with early ER-positive breast cancer may be identified who are at increased risk of recurrence within 2.5 years of diagnosis despite tamoxifen. It remains to be proven whether upfront AI therapy results in an advantage to these women.
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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 | 0.001 |
| 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".