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Record W2335344368 · doi:10.1097/ogx.0b013e3182877729

Bevacizumab Added to Neoadjuvant Chemotherapy for Breast Cancer

2013· article· en· W2335344368 on OpenAlexaff
Harry D. Bear, Gong Tang, Priya Rastogi, Charles E. Geyer, André Robidoux, James N. Atkins, Luis Báez-Díaz, Adam Brufsky, Rita S. Mehta, Louis Fehrenbacher, James A. Young, Francis M. Senecal, Rakesh Gaur, Richard G. Margolese, Paul T. Adams, Howard M. Gross, Joseph P. Costantino, Sandra M. Swain, Eleftherios P. Mamounas, Normal Wolmark

Bibliographic record

VenueObstetrical & Gynecological Survey · 2013
Typearticle
Languageen
FieldMedicine
TopicCancer Treatment and Pharmacology
Canadian institutionsCentre Hospitalier de l’Université de MontréalJewish General Hospital
Fundersnot available
KeywordsDocetaxelMedicineCapecitabineGemcitabineBevacizumabBreast cancerOncologyCyclophosphamideChemotherapyInternal medicineDoxorubicinCancerColorectal cancer

Abstract

fetched live from OpenAlex

Neoadjuvant chemotherapy can increase the rates of breast-conserving surgery in patients with operable breast cancer and is a reasonable alternative to adjuvant chemotherapy. Several trials have shown that adding bevacizumab (an antiangiogenic monoclonal antibody against vascular endothelial growth factor A) and the antimetabolites capecitabine and gemcitabine to taxanes can improve outcomes in patients with metastatic breast cancer. This trial investigated whether adding capecitabine or gemcitabine to neoadjuvant chemotherapy with docetaxel, followed by doxorubicin plus cyclophosphamide in women with operable human epidermal growth factor receptor 2 (HER2)–negative breast cancer, would increase the rates of pathological complete response in the breast. The pathological response after the addition of bevacizumab to these neoadjuvant chemotherapy regimens also was investigated; this was the primary end point of this study. A total of 1206 women with primary operable HER2-negative breast cancer were randomly assigned to 1 of 3 neoadjuvant chemotherapy regimens: (1) docetaxel (100 mg/m2 of body-surface area), administered on day 1 in 4 cycles every 3 weeks, followed by doxorubicin-cyclophosphamide (60 mg and 600 mg/m2, respectively), administered every 3 weeks (docetaxel group); (2) capecitabine (825 mg/m2), administered twice daily on days 1 through 14, added to docetaxel (75 mg/m2), administered on day 1 in 4 cycles, followed by doxorubicin-cyclophosphamide (docetaxel-capecitabine group); or (3) gemcitabine (1000 mg/m2), administered intravenously on days 1 and 8, added to docetaxel (75 mg/m2), administered on day 1 in 4 cycles, followed by treatment with doxorubicin-cyclophosphamide for 4 cycles (docetaxel-gemcitabine group). Patients also were randomized to receive or not receive bevacizumab (15 mg per kg of body weight), administered every 3 weeks, with each of the first 6 cycles of chemotherapy. Compared with adding docetaxel therapy alone, the addition of capecitabine or gemcitabine did not significantly increase the rate of pathological complete response in the breast (32.7% with docetaxel, 29.7% with docetaxel-capecitabine, and 31.8% with docetaxel-gemcitabine; P = 0.69). Addition of capecitabine or gemcitabine increased toxic effects, specifically, the hand-foot syndrome, neutropenia, and mucositis. Adding bevacizumab was associated with a significant increase in the rate of pathological complete response in the breast (with and without bevacizumab: 34.5 vs 28.2%, respectively; P = 0.02). The greatest benefit of adding bevacizumab was found in the patient subgroup with hormone receptor–positive tumors; there was a weaker effect in the hormone receptor–negative subgroup. These findings show that the addition of bevacizumab to neoadjuvant chemotherapy is associated with a small but significant increase in the rate of pathological complete response.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.046
GPT teacher head0.354
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreReview

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".

Quick stats

Citations81
Published2013
Admission routes1
Has abstractyes

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