Evidence map›Paper›PMID 42217017›Full record

ArticleInternational urogynecology journal2026

Shaping AI in Pelvic Floor Physiotherapy: The Impact of Role-Play Prompting on ChatGPT Response Quality.

Betul Cinar, Ertugrul Safran, Zeynep Ayyildiz-Eroglu

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Article in International urogynecology journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Betul CinarDepartment of Physiotherapy and Rehabilitation, Faculty of Health Sciences, Bezmialem Vakif University, Istanbul, Türkiye. betulbirik@gmail.com.ORCID http://orcid.org/0000-0002-0691-0780
Ertugrul SafranDepartment of Physiotherapy and Rehabilitation, Faculty of Health Sciences, Bezmialem Vakif University, Istanbul, Türkiye.ORCID http://orcid.org/0000-0002-6835-5428
Zeynep Ayyildiz-ErogluDutch Scoliosis Center, Utrecht University Medical Center, Utrecht, the Netherlands.ORCID http://orcid.org/0000-0003-3330-9031

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introduction and hypothesisPelvic floor physiotherapy (PFP) is a highly specialized field requiring complex and multidisiplinary clinical reasoning. While large language models (LLMs) are increasingly being utilized to support clinical education and decision-making in this domain, the relevance, accuracy, and comprehensiveness of their outputs vary significantly depending on prompt engineering. The aim of this study was to examine the impact of persona-based role-play prompting on the quality of ChatGPT responses to clinical questions in PFP.

methodsTwenty-one open-ended clinical questions covering assessment and management of common pelvic floor dysfunctions were presented to ChatGPT (GPT-4.1) under two conditions: (1) a neutral prompt and (2) a role-play prompt instructing the model to respond as an experienced pelvic floor physiotherapist. Two independent physiotherapists rated all responses across four domains-relevance, accuracy, comprehensiveness, and clarity-using a five-point rubric.

resultsPersona-based prompting significantly improved response quality across all domains (p < 0.001). The largest enhancement was observed in comprehensiveness (mean difference; 1.45), followed by relevance, accuracy, and clarity (mean difference; 1.12, 0.88, and 1.48, respectively). Effect sizes were large to very large (Cohen's d; 1.66-2.11). Inter-rater reliability ranged from moderate to excellent (ICC; 0.61-0.81).

conclusionsPersona-based role-play prompting markedly enhances the quality of LLM-generated responses in PFP. For clinicians, educators, and students, adopting structured prompts will substantially improve output quality; however, because accuracy remains imperfect, all generated responses still require careful professional oversight.

Indexed as

Artificial intelligencePelvic floor dysfunctionPrompt engineeringRehabilitationUrogynecology

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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.