Bevacizumab Added to Neoadjuvant Chemotherapy for Breast Cancer
Bibliographic record
Abstract
BACKGROUND: Bevacizumab and the antimetabolites capecitabine and gemcitabine have been shown to improve outcomes when added to taxanes in patients with metastatic breast cancer. The primary aims of this trial were to determine whether the addition of capecitabine or gemcitabine to neoadjuvant chemotherapy with docetaxel, followed by doxorubicin plus cyclophosphamide, would increase the rates of pathological complete response in the breast in women with operable, human epidermal growth factor receptor 2 (HER2)-negative breast cancer and whether adding bevacizumab to these chemotherapy regimens would increase the rates of pathological complete response. METHODS: We randomly assigned 1206 patients to receive neoadjuvant therapy consisting of docetaxel (100 mg per square meter of body-surface area on day 1), docetaxel (75 mg per square meter on day 1) plus capecitabine (825 mg per square meter twice a day on days 1 to 14), or docetaxel (75 mg per square meter on day 1) plus gemcitabine (1000 mg per square meter on days 1 and 8) for four cycles, with all regimens followed by treatment with doxorubicin-cyclophosphamide for four cycles. Patients were also randomly assigned to receive or not to receive bevacizumab (15 mg per kilogram of body weight) for the first six cycles of chemotherapy. RESULTS: The addition of capecitabine or gemcitabine to docetaxel therapy, as compared with docetaxel therapy alone, did not significantly increase the rate of pathological complete response (29.7% and 31.8%, respectively, vs. 32.7%; P=0.69). Both capecitabine and gemcitabine were associated with increased toxic effects--specifically, the hand-foot syndrome, mucositis, and neutropenia. The addition of bevacizumab significantly increased the rate of pathological complete response (28.2% without bevacizumab vs. 34.5% with bevacizumab, P=0.02). The effect of bevacizumab on the rate of pathological complete response was not the same in the hormone-receptor-positive and hormone-receptor-negative subgroups. The addition of bevacizumab increased the rates of hypertension, left ventricular systolic dysfunction, the hand-foot syndrome, and mucositis. CONCLUSIONS: The addition of bevacizumab to neoadjuvant chemotherapy significantly increased the rate of pathological complete response, which was the primary end point of this study. (Funded by the National Cancer Institute and others; ClinicalTrials.gov number, NCT00408408.).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".