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Identifying Intersecting Factors Associated With Suicidal Thoughts and Behaviors Among Transgender and Gender Diverse Adults: Preliminary Conditional Inference Tree Analysis

Amelia M Stanton, Lauren A Trichtinger, Norik Kirakosian, Simon M Li, Katherine E Kabel, Kiyan Irani, Alexandra H Bettis, Conall O’Cleirigh, Richard T Liu, Qimin Liu

J Med Internet Res 2025;27:e65452

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Sociodemographic Differences in Logins and Engagement With the Electronic Health Coach Messaging Feature of a Mobile App to Support Opioid and Stimulant Use Recovery: Results From a 1-Month Observational Study

Lindsey M Filiatreau, Hannah Szlyk, Alex T Ramsey, Erin Kasson, Xiao Li, Zhuoran Zhang, Patricia Cavazos-Rehg

JMIR Mhealth Uhealth 2025;13:e54753

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Authors’ Reply: Is the Pinball Machine a Blind Spot in Serious Games Research?

Luis Carlos Rodríguez Timaná, Javier Ferney Castillo García, Teodiano Bastos Filho, Alvaro Alexander Ocampo González, Nazly Rocio Hincapié Monsalve, Nicolas Jacobo Valencia Jimenez

JMIR Serious Games 2025;13:e73034

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Evaluation of the Tu’Washindi Na PrEP Intervention to Reduce Gender-Based Violence and Increase Preexposure Prophylaxis Uptake and Adherence Among Kenyan Adolescent Girls and Young Women: Protocol for a Cluster Randomized Controlled Trial

Sarah T Roberts, Alexandra M Minnis, Sue Napierala, Elizabeth T Montgomery, Lina Digolo, Mackenzie L Cottrell, Erica N Browne, Jacqueline Ndirangu, Joyce Boke, Kawango Agot

JMIR Res Protoc 2025;14:e55931

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Design and Baseline Evaluation of Social Media Vaping Prevention Trial: Randomized Controlled Trial Study

William Douglas Evans, Megumi Ichimiya, Jeffrey B Bingenheimer, Jennifer Cantrell, Alexander P D'Esterre, Olivia Pincus, Linda Q Yu, Elizabeth C Hair

J Med Internet Res 2025;27:e72002

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Unsupervised Deep Learning of Electronic Health Records to Characterize Heterogeneity Across Alzheimer Disease and Related Dementias: Cross-Sectional Study

Alexander et al [21] found the following 5 patient subtypes: mental health, nontypical AD, typical AD, CVD, and men with cancer. They later identified a consistent subtype with early-onset AD, predominantly female participants, with a faster rate of progression using various machine learning methods [22]. Landi et al [23] used unsupervised deep learning to encode EHRs with temporal information, identifying early-onset AD, later-onset AD with mild comorbidities, and typical-onset AD with moderate symptoms.

Matthew West, You Cheng, Yingnan He, Yu Leng, Colin Magdamo, Bradley T Hyman, John R Dickson, Alberto Serrano-Pozo, Deborah Blacker, Sudeshna Das