The Sources and Popularity of Online Drug Information: An Analysis of Top Search Engine Results and Web Page Views
Bibliographic record
Abstract
BACKGROUND: The Internet has become a popular source of health information. However, there is little information on what drug information and which Web sites are being searched. OBJECTIVE: To investigate the sources of online information about prescription drugs by assessing the most common Web sites returned in online drug searches and to assess the comparative popularity of Web pages for particular drugs. METHODS: This was a cross-sectional study of search results for the most commonly dispensed drugs in the US (n=278 active ingredients) on 4 popular search engines: Bing, Google (both US and Canada), and Yahoo. We determined the number of times a Web site appeared as the first result. A linked retrospective analysis counted Wikipedia page hits for each of these drugs in 2008 and 2009. RESULTS: About three quarters of the first result on Google USA for both brand and generic names linked to the National Library of Medicine. In contrast, Wikipedia was the first result for approximately 80% of generic name searches on the other 3 sites. On these other sites, over two thirds of brand name searches led to industry-sponsored sites. The Wikipedia pages with the highest number of hits were mainly for opiates, benzodiazepines, antibiotics, and antidepressants. CONCLUSIONS: Wikipedia and the National Library of Medicine rank highly in online drug searches. Further, our results suggest that patients most often seek information on drugs with the potential for dependence, for stigmatized conditions, that have received media attention, and for episodic treatments. Quality improvement efforts should focus on these drugs.
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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 | 0.005 | 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 | 0.001 |
| 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".