Quality of Online Information for Management of Leg Pain in Children and Adolescents With Hypermobility-Associated Conditions
Synopsis
Using the search terms 'management of lower limb pain in children with hypermobility-associated conditions', this study collated webpages from Google searches and the corresponding overviews presented by Gemini, assessed webpage quality with the Health Information Website Evaluation Tool and AI overview quality with the Quality Assessment of Medical Artificial Intelligence, and evaluating 20 webpages and four AI-generated overviews found that webpage quality was mostly moderate (14 moderate, three good) while all AI overviews were rated good, with accuracy the lowest-scoring domain for webpages and completeness and provision of resources and references the lowest-scoring domains for AI overviews.
Interpretation
The study assessed two types of online information, search engine webpages and AI-generated overviews, for a patient population with low satisfaction and disengagement from health professionals and services, namely hypermobility-associated conditions. Prior quality assessments of online health information have largely focused on webpages; this study places search engine results and corresponding AI overviews side by side within the same search scenario. Based on explicit search terms and two standardized assessment tools, with a sample of 20 webpages and four AI overviews, limited in size but clearly described in procedure.
Webpage information quality was overall moderate to good, with 14 webpages rated moderate and three rated good, and the two reviewers assigned different ratings to three webpages. Provides a concrete distribution of webpage usability for this specific condition and symptom combination, and shows that reviewer ratings diverged for some webpages. Used the Health Information Website Evaluation Tool, scored by two reviewers, and reported the number of webpages with rating disagreement.
AI-generated overviews were all considered good quality, but their lowest-scoring domains were completeness and provision of resources and references. Adds quality evidence for AI overviews as an emerging information channel beyond webpages, and identifies completeness and cited resources as the weaker domains. Used the Quality Assessment of Medical Artificial Intelligence, evaluating four AI overviews, a small sample but with a targeted tool.
The lowest-scoring domain for webpages was accuracy, and the authors accordingly advise online information seekers to review the references informing content, particularly when AI-generated. Translates quality assessment results into a concrete verification recommendation for information seekers, emphasizing the existence and relevance of references. The conclusion follows directly from the domain scores of the assessment tools and the authors' interpretation of the results.
Perspective
The study delineates the quality landscape of online information for management of lower limb pain in children and adolescents with hypermobility-associated conditions under a specific combination of search terms, search engine, and AI assistant, and is relevant to clinicians, caregivers, and information seekers who want to understand the usability of webpages and AI overviews in this scenario; its assessment framework can be extended to quality evaluation of online information for other conditions or symptoms.
The samples of webpages and AI overviews are small, and the AI overviews come from a single assistant, so whether the results are stable across different search terms, regions, or time points remains to be observed; the two reviewers disagreed on ratings for some webpages, suggesting that applying the rating criteria in borderline cases still needs further clarification; in addition, the text available here is summary-level content without figures or full methodological detail, so a complete reconstruction of the scoring rules and search process remains an open question.