Front-Line Bevacizumab plus Chemotherapy with or without Maintenance Therapy for Metastatic Breast Cancer: An Observational Study by the Hellenic Oncology Research Group
Bibliographic record
Abstract
Front-line bevacizumab (BEV) in combination with taxanes offers benefit in progression-free survival (PFS) in metastatic breast cancer (mBC). The medical records of mBC patients, treated with front-line BEV-based chemotherapy, were retrospectively reviewed in order to generate real life safety and efficacy data. Patients with human epidermal growth factor receptor 2 (HER2)-negative mBC treated with front-line BEV in combination with chemotherapy were eligible. Maintenance therapy with BEV and/or hormonal agents was at the physicians’ discretion. Among the 387 included patients, the most common adverse events were anemia (61.9%, mainly grade 1), grade 3/4 neutropenia (16.5%), grade 1/2 fatigue (22.3%), and grade 1/2 neuropathy (19.6%). Dose reductions were required in 164 cycles (7.1%) and toxicity led to treatment discontinuation in 21 patients (5.4%). The median PFS and the median overall survival (OS) were 13.3 (95% CI: 11.7–14.8) and 32.3 months (95% CI: 27.7–36.9), respectively. Maintenance therapy, with hormonal agents (ET) and/or BEV, was associated with longer OS versus no maintenance therapy (47.2 versus 23.6 months; p < 0.001) in patients with hormone receptor (HR)-positive disease and BEV maintenance offered longer OS versus no maintenance in patients with HR-negative disease (52.8 versus 23.3; p = 0.023). These real-life data show that front-line BEV-based chemotherapy in HER2-negative mBC patients is an effective treatment with an acceptable toxicity profile. The potential benefit of maintenance treatment, especially ET, is important and warrants further research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".