Bevacizumab for advanced breast cancer: Hope, hype, and hundreds of headlines.
Bibliographic record
Abstract
e11597 Background: On February 22, 2008, the Food and Drug Administration (FDA) granted accelerated approval for the use of bevacizumab (Avastin) in metastatic breast cancer. Based on the results of subsequent clinical trials this accelerated approval was revoked on November 18, 2011.In this study we categorize and analyze the newspaper reports related to bevacizumab use in advanced breast cancer. Methods: Using the FACTIVA media database, we reviewed all newspaper reports published in the United States and Canada from January 4,2002 to January 4, 2012 with the words “breast cancer” and “Avastin” or “bevacizumab”. Articles were classified as pre-approval (Jan 4 2002 – Feb 21 2012); approval (Feb 22 2008 – Nov 17 2011); or post-approval loss (Nov 18 2011 – Jan 4 2012). Information related to benefits, side effects, costs, interviewees, and article tone and theme were abstracted from each article by two independent reviewers. Differences between the 3 study phases were compared using the Chi square test. Results: 353 articles met our study inclusion criteria. The major article themes evolved over each study period. Reports having a positive headline tone (36%, 18%, 9%; p=0.0004) and/or positive article tone (42%, 19%, 9%; p<0.0001) declined with each study period. The proportion of articles discussing side effects (25%, 49%, 60%; p<0.0001) and financial costs (19%, 46%, 41%; p<0.0001) increased while those discussing efficacy decreased (82%, 59%, 15%; p<0.0001) with each study period. Drug representatives were most likely to be quoted in newspaper articles prior to bevacizumab’s approval (33%, 23%, 11%; p=0.015). Conclusions: Media reports are a common source of medical information for patients, practitioners, and policy-makers. Although we cannot discern cause from effect, we did observe substantial fluidity of media reports over time.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".