Urologists in cyberspace. A review of the quality of health information from American urologists’ websites using three validated tools
Bibliographic record
Abstract
OBJECTIVES: In this paper, we evaluate a sample of urologists' web-sites, based in the United States, using three validated instruments: the Health on the Net Foundation code of conduct (HONcode), DISCERN and LIDA tools. We also discuss how medical websites can be improved. METHODS: We used the 10 most populous cities in America, identified from the US Census Bureau, and searched using www.google. com to find the first 10 websites using the terms "urologist + city." Each website was scored using the HONcode, DISCERN and LIDA instruments. The median score for each tool was used to dichotomize the cohort and multivariable logistic regression was used to identify independent predictors of higher scores. RESULTS: Of the 100 websites found, 78 were analyzed. There were 18 academic institutions, 43 group and 17 solo practices. A medical website design service had been used by 18 websites. The HONcode badge was seen on 3 websites (4%). Social media was used by 16 websites. Multivariable logistic regression showed predictors of higher scores for each tool. For HONcode, academic centres (OR 6.8, CI 1.2-37.3, p = 0.028) and the use of a medical website design service (OR 17.2, CI 3.8-78.1, p = 0.001) predicted a higher score. With DISCERN, academic centres (OR 23.13, p = 0.002, CI 3.15-169.9 and group practices (OR 7.19, p = 0.022, CI 1.33-38.93) were predictors of higher scores. Finally, with the LIDA tool, there were no predictors of higher scores. Pearson correlation did not show any correlation between the three scores. CONCLUSIONS: Using 3 validated tools for appraising online health information, we found a wide variation in the quality of urologists' websites in the United States. Increased awareness of standards and available resources, coupled with guidance from health professional regulatory bodies, would improve the quality urological health information on medical websites.
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".