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Media Discourse, Influence, and Reflection: Content Analysis and Text-Mining Study of Suicides and Homicides in Long-Term Care

Media Discourse, Influence, and Reflection: Content Analysis and Text-Mining Study of Suicides and Homicides in Long-Term Care

On the line graph, the numbers within the circles indicate the number of news articles each year that included long-term care information or suicide prevention information, and the y-axis represents the proportion of long-term care information or suicide prevention information provided, calculated by dividing the number within the circle by the total number of news articles for that year.

Charlotte Wang, Hsiu-Ju Fang, Hsin-Yang Lu, Chen-Fen Chen

J Med Internet Res 2025;27:e59037

Relationship Between Within-Session Digital Motor Skill Acquisition and Alzheimer Disease Risk Factors Among the MindCrowd Cohort: Cross-Sectional Descriptive Study

Relationship Between Within-Session Digital Motor Skill Acquisition and Alzheimer Disease Risk Factors Among the MindCrowd Cohort: Cross-Sectional Descriptive Study

Y-axis represents the probability a participant is classified as a carrier or noncarrier based on their mean response time. The blue line represents the binomial relationship between response time and carrier status with faster response time more associated with being a carrier and slower response time more associated with being a noncarrier. The gray ribbon represents the 95% CI of the estimated probability of binomial relationship. (B) Mean Super G time in target between female and male participants.

Andrew Hooyman, Matt J Huentelman, Matt De Both, Lee Ryan, Kevin Duff, Sydney Y Schaefer

JMIR Aging 2025;8:e67298

Prediction of Reactivation After Antivascular Endothelial Growth Factor Monotherapy for Retinopathy of Prematurity: Multimodal Machine Learning Model Study

Prediction of Reactivation After Antivascular Endothelial Growth Factor Monotherapy for Retinopathy of Prematurity: Multimodal Machine Learning Model Study

Subsequently, these rankings were visualized using scatter plots, with each feature represented along the x-axis and its importance rank along the y-axis. This comprehensive visualization offered a comparative analysis of feature contributions across different algorithms, providing insights into the robustness and consistency of the feature importance.

Rong Wu, Yu Zhang, Peijie Huang, Yiying Xie, Jianxun Wang, Shuangyong Wang, Qiuxia Lin, Yichen Bai, Songfu Feng, Nian Cai, Xiaohe Lu

J Med Internet Res 2025;27:e60367

The Effectiveness of a Race-Based Stress Reduction Intervention on Improving Stress-Related Symptoms and Inflammation in African American Women at Risk for Cardiometabolic Disease: Protocol for Recruitment and Intervention for a Randomized Controlled Trial

The Effectiveness of a Race-Based Stress Reduction Intervention on Improving Stress-Related Symptoms and Inflammation in African American Women at Risk for Cardiometabolic Disease: Protocol for Recruitment and Intervention for a Randomized Controlled Trial

We will send recruitment letters describing the study to African American women (aged 50-75 y) who broadly meet eligibility criteria (eg, no history of myocardial infarction or ischemic stroke) from our respective hospital databases. In addition, we will recruit women through community clinics, churches, health fairs, hair salons, social media, newspaper advertisements, and word of mouth.

Karen L Saban, Cara Joyce, Alexandria Nyembwe, Linda Janusek, Dina Tell, Paula de la Pena, Darnell Motley, Lamise Shawahin, Laura Prescott, Stephanie Potts-Thompson, Jacquelyn Y Taylor

JMIR Res Protoc 2025;14:e65649

Daily Automated Prediction of Delirium Risk in Hospitalized Patients: Model Development and Validation

Daily Automated Prediction of Delirium Risk in Hospitalized Patients: Model Development and Validation

Reliability diagrams for different model types (rows) and patient subsets (columns) showing the actual fraction of patient snapshots with delirium for groups with a given predicted risk of delirium (blue squares, left y-axis). Error bars show the bootstrap 95% CI. The gray bars in the background show the number of patient snapshots in each predicted probability bin (y-axis on the right). ECE and MCE are with a 95% CI. ECE: expected calibration error; MCE: maximum calibration error.

Kendrick Matthew Shaw, Yu-Ping Shao, Manohar Ghanta, Valdery Moura Junior, Eyal Y Kimchi, Timothy T Houle, Oluwaseun Akeju, Michael Brandon Westover

JMIR Med Inform 2025;13:e60442