Bevacizumab for Advanced Breast Cancer: Hope, Hype, and Hundreds of Headlines
Bibliographic record
Abstract
On February 22, 2008, the Food and Drug Administration granted accelerated approval for the use of bevacizumab (Avastin) in metastatic breast cancer. Based on subsequent clinical trials, this approval was revoked on November 18, 2011. In this study, we categorize and analyze the newspaper reports related to bevacizumab's use in advanced breast cancer. Methods. Using the Factiva media database, we reviewed all newspaper reports published in North America from January 4, 2002, to January 4, 2013, containing the words "breast cancer" and "Avastin," or "bevacizumab." Articles were classified as pre-approval (January 4, 2002-February 21, 2008), approval (February 22, 2008-November 17, 2011), or post-approval loss (November 18, 2011-January 4, 2013). Information regarding benefits, side effects, costs, interviewees, and article tone and theme were abstracted from each article by two independent reviewers. Differences among the three study phases were compared using the chi square analysis. Results. A total of 359 articles met study inclusion criteria. The number of reports having a positive headline tone and/or positive article tone declined with each study period. The proportion of articles discussing side effects and financial costs increased, whereas those discussing efficacy decreased with each study period. Drug representatives were most likely to be quoted in newspaper articles prior to bevacizumab's approval. Conclusion. Media reports are a common source of medical information for patients, practitioners, and policy makers. We observed substantial fluidity of media reports over time.
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 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) | 0.000 | 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".