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Online Availability of Diamond Shruumz Before and After FDA Recall Initiation: Qualitative Assessment and Simulated Test Purchasing

Online Availability of Diamond Shruumz Before and After FDA Recall Initiation: Qualitative Assessment and Simulated Test Purchasing

For internet search queries, a total of 2509 hyperlinks, comprising 237 unique domain names, were collected; 67 (28.27%) domains were identified as actively selling Diamond Shruumz products prior to recall. From the sellers with sufficient information to cross-reference available state cannabis licensure data, none were linked to a valid retail cannabis state license. All websites that were actively selling Diamond Shruumz went through web forensics analysis.

Tim Mackey, Matthew Nali, Meng Zhen Larsen, Zhuoran Li, Jiawei Li, Joshua Yang

J Med Internet Res 2025;27:e64820

College Community–Based Physical Activity Support at a Public University During the COVID-19 Pandemic: Retrospective Longitudinal Analysis of Intra- Versus Interpersonal Components for Uptake and Outcome Association

College Community–Based Physical Activity Support at a Public University During the COVID-19 Pandemic: Retrospective Longitudinal Analysis of Intra- Versus Interpersonal Components for Uptake and Outcome Association

This study aimed to build upon prior findings from clinical trials by retrospective observation of an m Health PA promotion program incorporating both intrapersonal and interpersonal components, which was delivered with campus-wide outreach to students and faculty and staff.

Garrett I Ash, Selene S Mak, Adrian D Haughton, Madilyn Augustine, Phillip O Bodurtha, Robert S Axtell, Beatrice Borsari, Jason J Liu, Shaoke Lou, Xin Xin, Lisa M Fucito, Sangchoon Jeon, Matthew Stults-Kolehmainen, Mark B Gerstein

JMIR Mhealth Uhealth 2025;13:e51707

Comparison of Multimodal Deep Learning Approaches for Predicting Clinical Deterioration in Ward Patients: Observational Cohort Study

Comparison of Multimodal Deep Learning Approaches for Predicting Clinical Deterioration in Ward Patients: Observational Cohort Study

Prior research has shown that machine learning models often achieve superior performance compared to commonly used rule-based tools, such as the Modified Early Warning Score and the National Early Warning Score [1,7-11]. More importantly, using machine learning models for clinical deterioration as decision support tools in real-world hospital settings has been associated with decreased mortality [12,13]. However, such models still have room for improvement.

Charles A Kotula, Jennie Martin, Kyle A Carey, Dana P Edelson, Dmitriy Dligach, Anoop Mayampurath, Majid Afshar, Matthew M Churpek

J Med Internet Res 2025;27:e75340