Media Reporting of Practice-Changing Clinical Trials in Oncology: A North American Perspective
Bibliographic record
Abstract
INTRODUCTION: Media reporting of clinical trials impacts patient-oncologist interactions. We sought to characterize the accuracy of media and Internet reporting of practice-changing clinical trials in oncology. MATERIALS AND METHODS: The first media articles referencing 17 practice-changing clinical trials were collected from 4 media outlets: newspapers, cable news, cancer websites, and industry websites. Measured outcomes were media reporting score, social media score, and academic citation score. The media reporting score was a measure of completeness of information detailed in media articles as scored by a 15-point scoring instrument. The social media score represented the ubiquity of social media presence referencing 17 practice-changing clinical trials in cancer as determined by the American Society of Clinical Oncology in its annual report, entitled Clinical Cancer Advances 2012; social media score was calculated from Twitter, Facebook, and Google searches. The academic citation score comprised total citations from Google Scholar plus the Scopus database, which represented the academic impact per clinical cancer advance. RESULTS: From 170 media articles, 107 (63%) had sufficient data for analysis. Cohen's κ coefficient demonstrated reliability of the media reporting score instrument with a coefficient of determination of 94%. Per the media reporting score, information was most complete from industry, followed by cancer websites, newspapers, and cable news. The most commonly omitted items, in descending order, were study limitations, exclusion criteria, conflict of interest, and other. The social media score was weakly correlated with academic citation score. CONCLUSION: Media outlets appear to have set a low bar for coverage of many practice-changing advances in oncology, with reports of scientific breakthroughs often omitting basic study facts and cautions, which may mislead the public. The media should be encouraged to use a standardized reporting template and provide accessible references to original source information whenever feasible.
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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 | 0.099 | 0.824 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".