Patient‐targeted websites on overactive bladder: What are our patients reading?
Bibliographic record
Abstract
AIMS: Patients often turn to the Internet for information on medical conditions. We sought to evaluate the quality and readability of highly visible websites on overactive bladder (OAB). METHODS: A survey of 42 consecutive patients attending outpatient urogynecology clinics was performed to identify the most commonly used Internet search engines and search terms for information on OAB. The three most commonly used search engines (Google, Bing, and Yahoo!) were then queried using the three most commonly used search terms. The first 20 relevant websites from each search were reviewed. After excluding duplicates, 35 websites were analyzed. Website quality of information on OAB was evaluated using the DISCERN score, JAMA benchmark criteria, and Health on the Net code (HONcode) accreditation status. Readability was assessed using the Simplified Measure of Gobbledygook (SMOG) and Dale-Chall indices. RESULTS: Websites were classified as advertisement/commercial (31%), health portal (29%), professional (26%), patient group (6%), and other (9%). The overall mean DISCERN score was 44 ± 18 (maximum possible score of 80). Three websites (9%) met all four JAMA benchmark criteria. Seventeen percent of websites provided adequate information on content authorship and contributions. Median SMOG and Dale-Chall indices were 9.9 (IQR 9.3-11.2) and 9.0 (IQR 8.1-9.4), respectively. Nine websites (26%) were HONcode certified. CONCLUSIONS: Popular websites on OAB are of low quality, written for a high school to college-level readership, and often lack adequate information to assess the potential for commercial bias. Patients should be cautioned that incomplete and potentially biased information on OAB is prevalent online.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".