Role of Herceptin<sup>®</sup> in Primary Breast Cancer: Views from North America and Europe
Bibliographic record
Abstract
Current therapeutic strategies for primary breast cancer aim to provide improvements in outcome with minimal toxicity to the patient. However, annual relapse rates of up to 12 to 13% during the first 10 years after treatment are seen, and although toxicity has been reduced, it remains a problem in a patient population that is largely asymptomatic. Thus, there is a clear need for more effective therapies. Amplification/overexpression of the human epidermal growth factor receptor-2 (HER2) is an early event in the development of a significant proportion of breast tumors. This abnormality has been shown to have a detrimental effect on prognosis, may predict the outcome of therapies such as tamoxifen and anthracyclines, and provides a target for the novel therapy, Herceptin. Herceptin is effective and well tolerated in the metastatic setting, making it an ideal candidate for use in adjuvant breast cancer therapy. This has led to the design of a number of trials that aim to provide conclusive evidence as rapidly as possible that Herceptin is well tolerated and effective in the adjuvant setting while also addressing the question of which regimen provides greatest benefit. This review describes these trials and explains how differences in practice between North America and Europe have influenced trial design.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".