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A Web-Based Tool to Perform a Values Clarification for Stroke Prevention in Patients With Atrial Fibrillation: Design and Preliminary Testing Study

A Web-Based Tool to Perform a Values Clarification for Stroke Prevention in Patients With Atrial Fibrillation: Design and Preliminary Testing Study

The overall SURE test, saying “yes” to all 4 components, was 61.2% (156/255) for the standard group, 66.5% (145/218) for the visual group, and 67% (134/200) for the visual+VC group (visual vs standard, odds ratio [OR] 1.26, 95% CI 0.86‐1.84; P=.23; visual+VC vs standard, OR 1.29, 95% CI 0.87‐1.90; P=.20).

Michael P Dorsch, Allen J Flynn, Kaitlyn M Greer, Sabah Ganai, Geoffrey D Barnes, Brian Zikmund-Fisher

JMIR Cardio 2025;9:e67956

A Novel Just-in-Time Intervention for Promoting Safer Drinking Among College Students: App Testing Across 2 Independent Pre-Post Trials

A Novel Just-in-Time Intervention for Promoting Safer Drinking Among College Students: App Testing Across 2 Independent Pre-Post Trials

There was no significant effect of the study phase or incentives on any of the self-reported drinking outcomes, for the average number of days per week in the last month involving alcohol consumption (F2, 232=0.294, P=.75, η2=.003), typical weekend evening drink consumption in the last month (F2, 165=0.662, P=.52, η2=.008), the maximum number of drinks consumed in the last month (F2, 175=0.005, P=.99, η2=.00), or protective behavioral strategies (F2, 232=1.469, P=.23, η2=.013).

Philip I Chow, Jessica Smith, Ravjot Saini, Christina Frederick, Connie Clark, Maxwell Ritterband, Jennifer P Halbert, Kathryn Cheney, Katharine E Daniel, Karen S Ingersoll

JMIR Hum Factors 2025;12:e69873

Comparison of Deep Learning Approaches Using Chest Radiographs for Predicting Clinical Deterioration: Retrospective Observational Study

Comparison of Deep Learning Approaches Using Chest Radiographs for Predicting Clinical Deterioration: Retrospective Observational Study

The p-values of the AUROC scores are presented in Multimedia Appendix 1. As P Data cleaning and cohort selection with descriptive analysis were conducted using Stata version 16.1 (Stata Corp). We used Python version 3.8.10, along with the Monai framework version 1.2.0 (NVIDIA) and Pytorch version 2.0.0 (Facebook) to develop the deep learning models. Additionally, the AUROC score and its 95% CI were calculated using Fast De Long implementation from VMAF (Video Multimethod Assessment Fusion; Netflix) [43].

Mahmudur Rahman, Jifan Gao, Kyle A Carey, Dana P Edelson, Askar Afshar, John W Garrett, Guanhua Chen, Majid Afshar, Matthew M Churpek

JMIR AI 2025;4:e67144

Association of Social Media Recruitment and Depression Among Racially and Ethnically Diverse Metabolic and Bariatric Surgery Candidates: Prospective Cohort Study

Association of Social Media Recruitment and Depression Among Racially and Ethnically Diverse Metabolic and Bariatric Surgery Candidates: Prospective Cohort Study

We performed all analyses using STATA (v.17.1, Stata Corp LP), with statistical significance set at a P value below 5%. Participant characteristics (N=380) stratified by recruitment method, social media (n=107), and nonsocial media (n=273) are presented in Table 1. Participants recruited through social media had a mean age of 47.27 (SD 1.02) years, whereas those in the nonsocial media group had a mean age of 47.38 (SD 0.73) years (P=.93).

Jackson M Francis, Sitapriya S Neti, Dhatri Polavarapu, Folefac Atem, Luyu Xie, Olivia Kapera, Matthew S Mathew, Elisa Marroquin, Carrie McAdams, Jeffrey Schellinger, Sophia Ngenge, Sachin Kukreja, Benjamin E Schneider, Jaime P Almandoz, Sarah E Messiah

JMIR Form Res 2025;9:e58916

Evaluating the Effectiveness of Large Language Models in Providing Patient Education for Chinese Patients With Ocular Myasthenia Gravis: Mixed Methods Study

Evaluating the Effectiveness of Large Language Models in Providing Patient Education for Chinese Patients With Ocular Myasthenia Gravis: Mixed Methods Study

The accuracy rates of 130 choice questions across 8 groups were compared using the chi-square test, with Bonferroni correction applied to adjust P values for detecting intergroup differences. For the 23 common questions, the Friedman test was used to compare each chatbot’s evaluation scores and readability scores, followed by post-hoc pairwise comparisons using the Dunnett test. A two-sample t test was used to compare the mean response scores of GPT o1-Preview and Ernie 3.5.

Bin Wei, Lili Yao, Xin Hu, Yuxiang Hu, Jie Rao, Yu Ji, Zhuoer Dong, Yichong Duan, Xiaorong Wu

J Med Internet Res 2025;27:e67883

Young Adult Perspectives on Artificial Intelligence–Based Medication Counseling in China: Discrete Choice Experiment

Young Adult Perspectives on Artificial Intelligence–Based Medication Counseling in China: Discrete Choice Experiment

Preferred service attributes included refined medication counseling services (with general medication counseling services as the reference; β=0.51; P The 100% level of symptom-specific results was strongly preferred by the respondents, and the same answer was obtained during the face-to-face conversations with the respondents.

Jia Zhang, Jing Wang, JingBo Zhang, XiaoQian Xia, ZiYun Zhou, XiaoMing Zhou, YiBo Wu

J Med Internet Res 2025;27:e67744

Examining Weight Suppression, Leptin Levels, Glucagon-Like Peptide 1 Response, and Reward-Related Constructs in Severity and Maintenance of Bulimic Syndromes: Protocol and Sample Characteristics for a Cross-Sectional and Longitudinal Study

Examining Weight Suppression, Leptin Levels, Glucagon-Like Peptide 1 Response, and Reward-Related Constructs in Severity and Maintenance of Bulimic Syndromes: Protocol and Sample Characteristics for a Cross-Sectional and Longitudinal Study

We observed stability in BMI across days (r>0.95; P The Eating Disorder Examination (EDE) 17.0 D [83] was selected due to previous evidence of good discriminant validity [84-87], IRR (0.83 to 0.99) [88,89], and good internal consistency of the restraint and body image subscales (Cronbach α>0.70) [90].

Pamela K Keel, Lindsay P Bodell, Sarrah I Ali, Austin Starkey, Jenna Trotta, J Woody Luxama, Chloé Halfhide, Naomi G Hill, Jonathan Appelbaum, Diana L Williams

JMIR Res Protoc 2025;14:e66554