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Correction: Use and Engagement With Low-Intensity Cognitive Behavioral Therapy Techniques Used Within an App to Support Worry Management: Content Analysis of Log Data

The authorship was previously published as: Paul Farrand1,2*, Ph D; Patrick J Raue3*, Ph D; Earlise Ward4*, Ph D; Dean Repper5*, MSc; Patricia Areán3*, Ph D The following author, equal contribution tag, ORCID, and associated affiliation have been added in the fifth position of the authorship: Jonathon Baker6*, MA (ORCID 0009 0000 9923 5042) Iona Mind Inc, Romford, United Kingdom The Authors' Contributions was revised from: PF, PA, and PJR conceptualized and designed the project with EW and DR providing theoretical

Paul Farrand, Patrick J Raue, Earlise Ward, Dean Repper, Jonathan Baker, Patricia Areán

JMIR Mhealth Uhealth 2025;13:e76573

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Analyzing Usage of the Metaverse by Associations of Patients With Prostate Cancer During the 2023 Blue Ribbon Campaign: Cross-Sectional Survey Study

Schematic overview of the metaverse associations of patients with prostate cancer, showing (A) the campaign introduction area, (B) the informational sector, (C) a live Q&A session, and (D) a segment of an educational session. Q&A: question and answer. Distribution and collection of the questionnaire for this study were managed by Enzaim Health, a health care marketing communications company.

Jung Ki Jo, Yeeun Kim, Yun-Sok Ha, Kwang Taek Kim, Sangjun Yoo, Woo Suk Choi, Jihye Yang, Jaeeun Shin, Sun Il Kim, Jeong Hyun Kim

J Med Internet Res 2025;27:e63030

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Peer Review of “The Order in Speech Disorder: A Scoping Review of State of the Art Machine Learning Methods for Clinical Speech Classification (Preprint)”

Vanessa Fairhurst, Sylvester Sakilay, Randa Salah Gomaa Mahmoud, Shailee Rasania, J Moonga, Toba Isaac Olatoye, Rameshwari Prasad, Prasakthi Venkatesan, Vasco Medeiros, Uday Kumar Chalwadi

JMIRx Med 2025;6:e76836

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Clinical Value of ChatGPT for Epilepsy Presurgical Decision-Making: Systematic Evaluation of Seizure Semiology Interpretation

Performance comparison on a publicly sourced data cohort between ZSP and FSP using Chat GPT-4: (A) NPIR distribution; (B) and (C) RSens. Figures 3 B and 3 C present the RSens values for each region. For the frontal lobe, GPT-4 ZSP achieved 0.90, and FSP achieved 0.88. For the temporal lobe, ZSP achieved 0.81, and FSP achieved 0.83. For the occipital lobe, both ZSP and FSP achieved 0.42. For the parietal lobe, ZSP achieved 0.26, and FSP achieved 0.22.

Yaxi Luo, Meng Jiao, Neel Fotedar, Jun-En Ding, Ioannis Karakis, Vikram R Rao, Melissa Asmar, Xiaochen Xian, Orwa Aboud, Yuxin Wen, Jack J Lin, Fang-Ming Hung, Hai Sun, Felix Rosenow, Feng Liu

J Med Internet Res 2025;27:e69173

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Evaluation and Bias Analysis of Large Language Models in Generating Synthetic Electronic Health Records: Comparative Study

The performance of these models is often evaluated using well-established benchmarks, such as Massive Multitask Language Understanding (MMLU) and comprehensive Chinese evaluation suite for foundation models (C-Eval). MMLU scores, commonly referenced in English-speaking academic circles [40], and C-Eval, a widely used metric in Chinese-speaking domains [41], provide critical indicators of model performance.

Ruochen Huang, Honghan Wu, Yuhan Yuan, Yifan Xu, Hao Qian, Changwei Zhang, Xin Wei, Shan Lu, Xin Zhang, Jingbao Kan, Cheng Wan, Yun Liu