Invasive disease-free survival benefit following neratinib as extended adjuvant therapy in centrally-confirmed HER2+ early-stage breast cancer: The ExteNET phase III randomized placebo-controlled trial.
Bibliographic record
Abstract
117 Background: Neratinib is an irreversible pan-HER tyrosine kinase inhibitor with clinical efficacy in trastuzumab pre-treated HER2-positive (HER2+) metastatic breast cancer (BC). ExteNET is an ongoing multicenter randomized placebo-controlled phase III trial evaluating the efficacy and safety of a 1-year course of neratinib in patients with early-stage HER2+ BC after trastuzumab-based adjuvant therapy (clinicaltrials.gov: NCT00878709). Methods: Women with locally-confirmed early-stage HER2+ BC were randomly assigned to oral neratinib 240mg/day or matching placebo for 1 year. Archived diagnostic tumor samples were submitted for HER2 gene amplification testing at a central laboratory. Primary endpoint: invasive disease-free survival (iDFS). Secondary endpoints: DFS including ductal carcinoma in situ (DFS+DCIS); distant disease-free survival (DDFS); time to distant recurrence (TDR). Stratified Cox proportional-hazards models were used to estimate hazard ratios (HR) for the ITT and amended ITT (aITT) populations; unstratified models were used for the centrally confirmed HER2 population. Treatment groups were compared using 2-sided log-rank tests. Results: The ITT population included 2840 patients (neratinib, N=1420; placebo, N=1420). The higher-risk aITT population (i.e. node-positive disease and randomized ≤1 year of completing prior trastuzumab) included 1873 patients (neratinib, N=938; placebo, N=935). Of the tumor samples analyzed, 1463 (86%) were centrally confirmed (neratinib, N=741; placebo, N=722). Conclusions: Neratinib significantly improves iDFS in trastuzumab-treated early-stage HER2+ BC patients. An enhanced treatment effect is observed with neratinib in women with centrally confirmed HER2+ tumors. Clinical trial information: NCT00878709. [Table: see text]
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".