Overall survival with neratinib after trastuzumab-based adjuvant therapy in HER2-positive breast cancer (ExteNET): A randomised, double-blind, placebo-controlled, phase 3 trial
Bibliographic record
Abstract
BACKGROUND: ExteNET showed that neratinib, an irreversible pan-HER tyrosine kinase inhibitor, given for 1 year after trastuzumab-based therapy significantly improved invasive disease-free survival in women with early-stage HER2-positive breast cancer. We report the final analysis of overall survival in ExteNET. METHODS: In this international, randomised, double-blind, placebo-controlled, phase 3 trial, women aged 18 years or older with stage 1-3c (amended to stage 2-3c) HER2-positive breast cancer who had completed neoadjuvant and adjuvant chemotherapy plus trastuzumab were eligible. Patients were randomly assigned to oral neratinib 240 mg/day or placebo for 1 year. Randomisation was stratified according to hormone receptor (HR) status (HR-positive vs. HR-negative), nodal status (0, 1-3 or 4+), and trastuzumab regimen (sequentially vs. concurrently with chemotherapy). Overall survival was analysed by intention to treat. ExteNET is registered (Clinicaltrials.gov: NCT00878709) and is complete. RESULTS: Between July 9, 2009, and October 24, 2011, 2840 women received neratinib (n = 1420) or placebo (n = 1420). After a median follow-up of 8.1 (IQR, 7.0-8.8) years, 127 patients (8.9%) in the neratinib group and 137 patients (9.6%) in the placebo group in the intention-to-treat population had died. Eight-year overall survival rates were 90.1% (95% CI 88.3-91.6) with neratinib and 90.2% (95% CI 88.4-91.7) with placebo (stratified hazard ratio 0.95; 95% CI 0.75-1.21; p = 0.6914). CONCLUSIONS: Overall survival in the extended adjuvant setting was comparable for neratinib and placebo after a median follow-up of 8.1 years in women with early-stage HER2-positive breast cancer.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".