Adjuvant and Neoadjuvant Therapy with Lapatinib in ErbB2-Overexpressing Breast Cancer
Bibliographic record
Abstract
Unanswered Questions in Adjuvant Trastuzumab TherapyFour adjuvant trials have demonstrated the benefit of adding trastuzumab to conventional chemotherapy in terms of improving recurrence-free and overall survival [6][7][8].The positive results of the first interim analyses were confirmed by updates of NCCTG N9831 and NSABP B-31 as well as Optimal Adjuvant Chemotherapy for ErbB2-Overexpressing Breast CancerAn area of intensive investigation is the identification of subsets of breast cancer patients who benefit from specific chemotherapeutic regimens.In their pooled analysis from 7 randomized trials, Gennari et al. [4] reported a 29% reduction in the risk of relapse and a 27% reduction in mortality for trastuzumab-naive patients with ErbB2-overexpressing breast tumors treated with anthracycline-based regimens compared to the ErbB2-negative cohort.The authors concluded that the superiority of these regimens seemed to be limited to ErbB2overexpressing breast cancer.The increased responsiveness of tumors with a positive ErbB2 status to anthracyclines was thought to be at least partially explained by coamplification of ErbB2 and topoisomerase II alpha (TOP 2A), the enzyme for
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".