<?xml version="1.0" encoding="UTF-8"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-10-28T02:56:27Z</responseDate><request verb="GetRecord" identifier="oai:kostu-rdlopen.rawdatalibrary.net:kostu/10371" metadataPrefix="oai_dc">https://kostu-rdlopen.rawdatalibrary.net/oai/request</request><GetRecord><record><header><identifier>oai:kostu-rdlopen.rawdatalibrary.net:kostu/10371</identifier><datestamp>2026-10-27T23:53:13Z</datestamp><setSpec>openaire</setSpec><setSpec>com_kostu_10000</setSpec><setSpec>col_kostu_10001</setSpec></header><metadata><oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><dc:title>Referential hallucination and clinical reliability in large language models: a comparative analysis using regenerative medicine guidelines for chronic pain</dc:title><dc:creator>Kuculmez, Ozlem</dc:creator><dc:creator>Usen, Ahmet</dc:creator><dc:creator>Ahi, Emine Dündar</dc:creator><dc:subject>Artificial intelligence</dc:subject><dc:subject>Guideline</dc:subject><dc:subject>Pain</dc:subject><dc:subject>Regeneration</dc:subject><dc:subject>Stem cell</dc:subject><dc:description>This study compared language models’ responses to open-ended questions on regenerative therapy guidelines for chronic pain, assessing their accuracy, reliability, usefulness, readability, semantic similarity, and hallucination rates. This cross-sectional study used 16 open-ended questions based on the American Society of Pain and Neuroscience’s regenerative therapy guidelines for chronic pain. Questions were answered by ChatGPT-4o, Gemini 2.5 Flash, and Claude 4 Opus. Responses were rated on a 7-point Likert scale for usability and reliability, and a 5-point scale for accuracy. Hallucinogenicity, readability (FKRE, FKGL), and similarity (USE, ROUGE-L) were also assessed. Statistical comparisons were made, with significance set at p &lt; 0.05. Claude Opus 4 showed the highest reliability (5.19 ± 1.11), usefulness (5.06 ± 1.0), and clinical accuracy (4.06 ± 0.68), outperforming ChatGPT-4o (4.13 ± 0.96; 3.94 ± 0.85; 3.38 ± 0.72) and Gemini 2.5 (4.19 ± 0.98; 4.06 ± 0.93; 3.38 ± 0.62). Claude had the lowest reference hallucinations (RHS 4.44 ± 3.18) vs. ChatGPT-4o (8.38 ± 1.86) and Gemini 2.5 (8.75 ± 1.73). In semantic similarity, Claude (0.68 ± 0.08) and Gemini (0.65 ± 0.07) surpassed ChatGPT-4o (0.60 ± 0.09). Gemini led in ROUGE-L F1 (0.12 ± 0.03) vs. Claude (0.10 ± 0.02) and ChatGPT-4o (0.07 ± 0.03). Readability was similar, though Gemini had a higher FKGL (11.3 ± 1.06) than Claude (10.3 ± 2.09). Claude Opus 4 showed superior accuracy, reliability, and usefulness, with significantly fewer hallucinations. Readability scores were similar across models. Further research is recommended.</dc:description><dc:publisher>Springer Science and Business Media Deutschland GmbH</dc:publisher><dc:date>2025</dc:date><dc:type>Article</dc:type><dc:identifier>https://rdlopen.rawdatalibrary.net/r/kostu/10371</dc:identifier><dc:identifier>https://doi.org/10.1007/s00296-025-05996-z</dc:identifier><dc:identifier>0172-8172</dc:identifier><dc:language>en</dc:language><dc:relation>Rheumatology International</dc:relation><dc:rights>info:eu-repo/semantics/closedAccess</dc:rights><dc:type>info:eu-repo/semantics/article</dc:type><dc:identifier>https://kostu-rdlopen.rawdatalibrary.net/tr/items/kostu/10371</dc:identifier></oai_dc:dc></metadata></record></GetRecord></OAI-PMH>