Use of Bevacizumab as a First-Line Treatment for Metastatic Breast Cancer
Bibliographic record
Abstract
OBJECTIVE: During clinical practice, it can be challenging, given the lack of response biomarkers, to identify the patients with metastatic breast cancer (mbca) who would benefit most from the addition of bevacizumab to first-line standard chemotherapy. The aim of the present review was to summarize the relevant scientific evidence and to discuss the experience of a group of experts in using bevacizumab to treat mbca. METHODS: A panel of 17 Spanish oncology experts met to discuss the literature and their experience in the use of bevacizumab as first-line treatment for mbca. During the meeting, discussions focused on three main issues: the profile of the patients who could benefit most from bevacizumab, the optimal bevacizumab treatment duration, and the safety profile of bevacizumab. RESULTS: The subset of mbca patients who would benefit the most from the addition of bevacizumab to first-line standard chemotherapy are those with clinically defined aggressive disease. Treatment with bevacizumab should be maintained until disease progression or the appearance of unacceptable toxicity. In the mbca setting, the toxicity profile of bevacizumab is well known and can be managed in clinical practice after adequate training. CONCLUSIONS: This expert group recommends administering bevacizumab as first-line treatment in patients with clinically aggressive disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".