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Early Detection of Type 1 Diabetes in First-Degree Relatives in Saudi Arabia (VISION-T1D): Protocol for a Pilot Implementation Study

Iman S Algadi, Yazed AlRuthia, Muhammad H Mujammami, Khaled Hani Aburisheh, Metib Alotaibi, Sharifah Al Issa, Amal A Al-Saif, David Seftel, Cheng-Ting Tsai, Reem A Al Khalifah

JMIR Res Protoc 2025;14:e70575

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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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Web-Based Explainable Machine Learning-Based Drug Surveillance for Predicting Sunitinib- and Sorafenib-Associated Thyroid Dysfunction: Model Development and Validation Study

The five cutoff points on the precision-recall curve (A) represent different percentages of outcome predictions, precision, recall, and F1-scores based on different thresholds; (B) ,The X-axis represents the value of the threshold and the Y-axis shows values of the precision, recall, and F1-score. Figure 4 shows model interpretation implemented with the SHAP analysis on the best-performing GBDT_RAW model.

Fan-Ying Chan, Yi-En Ku, Wen-Nung Lie, Hsiang-Yin Chen

JMIR Form Res 2025;9:e67767

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Co-Designing a Web-Based and Tablet App to Evaluate Clinical Outcomes of Early Psychosis Service Users in a Learning Health Care Network: User-Centered Design Workshop and Pilot Study

Kathleen E Burch, Valerie L Tryon, Katherine M Pierce, Laura M Tully, Sabrina Ereshefsky, Mark Savill, Leigh Smith, Adam B Wilcox, Christopher Komei Hakusui, Viviana E Padilla, Amanda P McNamara, Merissa Kado-Walton, Andrew J Padovani, Chelyah Miller, Madison J Miles, Nitasha Sharma, Khanh Linh H Nguyen, Yi Zhang, Tara A Niendam

JMIR Hum Factors 2025;12:e65889

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Anticipated Acceptability of Blended Learning Among Lay Health Care Workers in Malawi: Qualitative Analysis Guided by the Technology Acceptance Model

Ethical clearance was provided by the University of North Carolina at Chapel Hill institutional review board (#20‐1810), the Malawi National Health Sciences Research Committee (#20/06/2566) and the Baylor College of Medicine institutional review board (H-48800). Interviewers obtained written informed consent from all participants before starting the IDIs, reminding participants that their participation was voluntary and could be withdrawn at any time.

Tiwonge E Mbeya-Munkhondya, Caroline J Meek, Mtisunge Mphande, Tapiwa A Tembo, Mike J Chitani, Milenka Jean-Baptiste, Caroline Kumbuyo, Dhrutika Vansia, Katherine R Simon, Sarah E Rutstein, Victor Mwapasa, Vivian Go, Maria H Kim, Nora E Rosenberg