Efficacy of bevacizumab (BV) plus docetaxel (D) does not correlate with hypertension (HTN) or G-CSF use in patients (pts) with locally recurrent (LR) or metastatic breast cancer (mBC) in the AVADO phase III study.
Bibliographic record
Abstract
Abstract Abstract #1027 Background: BV (Avastin®), a monoclonal antibody to VEGF, significantly improved PFS and response rate in pts with LR or mBC when combined with 1st-line taxane CTx in two randomised phase III studies: E2100 (BV + weekly paclitaxel) and double-blind, PL-controlled study AVADO (two different BV doses + D). Efficacy of some biological agents correlates with certain toxicities. This was hypothesised for HTN with BV use and investigated in the AVADO study in an exploratory analysis. In contrast, CSF use in pts receiving CTx may adversely affect response, so correlation of G-CSF use with efficacy was also examined. Methods: This study compared BV 7.5 or 15mg/kg + D 100mg/m2 with PL + D in 736 pts with inoperable LR or mBC with no CTx 6 months prior to randomisation (12 months if taxane-based), ECOG PS 0–1 and adequate LVEF. D was administered q3w for up to 9 cycles, BV/PL q3w until disease progression or unacceptable toxicity. Results: The safety population consisted of 730 pts. Reported grade 3–4 HTN was low in the AVADO BV arms (1 pt, 0.4% [BV 7.5mg/kg]; 8 pts, 3.2% [BV 15mg/kg]), therefore the analysis of correlation with efficacy was performed for all grades of HTN. Data for HTN of all grades and G-CSF use in all randomised pts are shown below. Use of G-CSF did not affect dose intensity of D. Conclusions: Efficacy of BV + D in pts with LR or mBC did not appear to correlate with incidence of all grade HTN or G-CSF use. The apparent PFS and response benefit in the BV 7.5mg/kg arm for pts with HTN should be taken with caution as it is based on a small number of pts and may be due to an imbalance in baseline characteristics or a higher likelihood of detecting HTN in patients receiving study therapy for longer. Analysis of a larger dataset is needed to provide further data in this context. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 1027.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".