The use of bevacizumab among women with metastatic breast cancer: A survey on clinical practice and the ongoing controversy
Bibliographic record
Abstract
BACKGROUND: The US Food and Drug Administration's (FDA's) recent decision to remove the indication of bevacizumab for metastatic breast cancer (MBC) has fueled a debate in the breast cancer community. We conducted a survey to assess the perception of health care workers involved in the management of women with MBC on the FDA's decision to ascertain how it will affect practice and to determine how bevacizumab is commonly used in the community for MBC. METHODS: E-mails were sent out between September and November 2010 using a database of 3000 addresses maintained by the United Arab Emirates Cancer Congress. Individuals working for Roche or Genentech were excluded. The survey consisted of 22 questions that were divided into 3 parts addressing each participant's demographic profile, their opinion of the FDA's decision, and the typical use of bevacizumab in the community in the setting of MBC. RESULTS: A total of 564 participants were included in the final analysis, contributing to an 18.8% response rate. Of these participants, 14.6% were from the United States, 7.8% were from Canada, 31.1% were from Europe, 2.0% were from the United Arab Emirates, 11.1% were from Asia, and 33.3% were from other countries. The majority of participants believed progression-free survival to be a surrogate for overall survival, that cost played a role in the FDA's decision, and that the decision would adversely affect the future of newer drugs currently being investigated for MBC. The majority of participants indicated that they would use bevacizumab for triple receptor-negative MBC (46.5%), would use it in a first-line setting (44.7%), and would use it in combination with paclitaxel (51.9%). CONCLUSION: Our survey results highlight the discord between the opinion of community oncologists and the FDA's recent decision to withdraw the indication of bevacizumab for MBC.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".