Optimal adjuvant endocrine treatment of ER+/HER2+ breast cancer patients by age at diagnosis: A population-based cohort study
Bibliographic record
Abstract
BACKGROUND: patients; however, a differential effect of aromatase inhibitors (AIs) versus tamoxifen (TAM) may have been missed in ER+/HER2+ patients that comprise 7-15% of all breast cancer patients. In addition, a woman's hormonal microenvironment may influence sensitivity to TAM and AIs in the adjuvant setting, which changes during menopausal transition, a process that takes years. We studied the efficacy of AIs versus TAM in ER+/HER2+ breast cancer patients grouped by age at diagnosis as a proxy for menopausal status using treatment and outcome data from the nationwide population-based Netherlands Cancer Registry (NCR). PATIENTS AND METHODS: , ER+/HER2+ breast cancer were identified through the NCR (n = 1155). Patients were divided by age at diagnosis: premenopausal (≤45 years; n = 326), perimenopausal (45 55 years; n = 525). A time-dependent variable, indicating whether AI or TAM was received for >50% of endocrine treatment duration, was applied to subdivide groups by predominant treatment received. Recurrence-free survival (RFS) and overall survival (OS) were assessed using Kaplan-Meier survival estimation and Cox regression. Hazard ratios (HRs) were adjusted for chemotherapy, trastuzumab, age at diagnosis, N-status, grade, pT-stage and ovarian ablation. RESULTS: During follow-up, 237 recurrences and 182 deaths occurred. Perimenopausal women derived significant RFS and OS benefit from AI compared with TAM, HR 0.47 (95% CI 0.25-0.91; P = 0.03) and HR 0.37 (95% CI 0.18-0.79; P = 0.01), respectively, whereas premenopausal women derived no benefit from AI compared with TAM. Treatment effects differed significantly between these age groups (interaction P = 0.03 and P = 0.02, respectively). Among postmenopausal women a small but non-significant AI benefit was observed. CONCLUSION: AI treatment, preferably without any TAM treatment, was associated with the best RFS and OS outcome in ER+/HER2+ perimenopausal breast cancer patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".