Quality of Social Media and Web-Based Information Regarding Inappropriate Nuclear Cardiac Stress Testing and the Choosing Wisely Campaign: A Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: The World Wide Web and social media provide the public with access to medical information unlike any other time in human history. However, the quality of content related to cardiac stress testing is not well understood. OBJECTIVE: The aim of our study was to evaluate the quality of content on the Internet relating to the use of cardiac nuclear stress testing and the Choosing Wisely campaign. METHODS: We searched the World Wide Web, Google Video (including YouTube), and Twitter for information relating to these two topics. Searches were performed using English language terms from a computer in the United States not logged into any personal user accounts. Search results were reviewed for discussion of specific topics including radiation risk, accuracy of testing, alternative testing options, and discouragement of inappropriate test use. RESULTS: We evaluated a total of 348 items of content from our searches. Relevant search results for Choosing Wisely were fewer than for other search terms (45 vs 303). We did not find any content which encouraged inappropriate testing (ie, screening in low risk individuals or testing prior to low risk operations). Content related to Choosing Wisely was more likely to discourage inappropriate testing than search results for other terms (29/45, 64% vs 12/303, 4.0%, odds ratio 43.95, 95% CI 17.6-112.2, P<.001). CONCLUSIONS: The Internet content on nuclear stress tests consistently discouraged inappropriate testing. The Choosing Wisely content was more likely to discourage inappropriate testing, less relevant content was available. Generating authoritative content on the Internet relating to judicious use of medical interventions may be an important role for the Choosing Wisely campaign.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".