ArticleResearch square2026
AI-driven credibility profiling of real-world patient experiences suggests overlooked kidney stone therapies warrant further investigation.
Article in Research square, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Thousands of patients share their testimonials of kidney stone treatment efficacy online routinely, often with the hope of helping other patients understand what may help alleviate the severe suffering inflicted by the condition. Such testimonials generate vast data with potential hypothesis-generating insights that remain largely untapped. For this study, 4,899 publicly available online testimonials of nine different kidney stone products were collected across three different platforms: WebMD, Amazon, and Reddit. Using a state-of-the-art Large Language Model, these subjective reports were classified along multiple dimensions, focusing on reported effectiveness and rate of adverse effects. A methodology to minimize biases and extract statistically significant data from online testimonials is presented. Odds ratios and p-values were calculated using
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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 OpenQuestion graph.