ArticleInternational urogynecology journal2026
Assessment of AI-Generated Patient Education Materials for Bladder Training and Pelvic Floor Muscle Therapy: Comparison with an International Society Leaflet.
Article in International urogynecology journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Response to Letter to the Editor: "Assessment of AI-Generated Patient Education Materials for Bladder Training and Pelvic Floor Muscle Therapy: Comparison with an International Society Leaflet".International urogynecology journal · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introduction and hypothesisHigh-quality patient education materials are essential in urogynecology. We hypothesized that patient handouts generated by different large language models (LLMs) would vary in quality and readability and would differ from an established society-produced leaflet.
methodsTwelve leaflets on bladder training and pelvic floor muscle therapy from six origins: GPT-4, Gemini-2.5 Pro, Sonnet-4, Llama-4, Perplexity, and The International Urogynecological Association (IUGA), were produced or obtained and standardized into plain text. Three blinded reviewers assessed completeness, information quality (DISCERN), and the Patient Education Materials Assessment Tool (PEMAT-A: actionability; PEMAT-U: understandability). The statistical plan included ordinary least squares fixed-effects per metric with type II analysis of variance for source effects; estimated marginal means with Holm-adjusted pairwise comparisons; a crossed mixed-effects model for topic groups; and inter-rater reliability was measured. Readability and text analyses used standard indices.
resultsOrigins varied in completeness (p < 0.001), DISCERN (p < 0.001), and PEMAT-A (p = 0.0018); PEMAT-U showed a trend (p = 0.063). Llama-4 scored significantly lower on completeness and DISCERN, and lower than GPT-4, IUGA, and Perplexity on PEMAT-A; Sonnet4 outperformed Llama-4 on PEMAT-U. No single origin dominated all metrics. Readability varied greatly: GPT-4 had an average Flesch-Kincaid grade level ≈ 6.6, Gemini ≈ 7.4; Sonnet4 ≈ 15; Llama-4 ≈ 17. IUGA leaflets were the longest, with grade levels around 9-10. Bladder-training materials were modestly more complete than pelvic muscle materials (p = 0.045). Inter-rater reliability was high (ICC ≥ 0.87).
conclusionsPatient education quality varies substantially across AI tools and compared with society materials. AI-generated content can meet readability targets but requires expert review to ensure completeness and reliability before clinical use.
Indexed as
Identifiers
41998329What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.