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Development and Validation of a Depression Scale for Online Assessment: Cross-Sectional Observational Study

Development and Validation of a Depression Scale for Online Assessment: Cross-Sectional Observational Study

Numerous Korean studies, including those by Park et al [11], Song [12], Seo and Song [13], and Kim et al [14] have shown that SNS-based expressions—such as statements of hopelessness, isolation, or suicidal ideation—can be systematically analyzed to identify users at risk of depression. These findings suggest that digital expressions are not only reflections of individual distress but also potential signals for early detection.

Minjeong Jeon, Hae-In Park, Yoorianna Son, Ji Won Hyun, Jin Young Park

J Med Internet Res 2025;27:e70689

Comparing the Performance of Machine Learning Models and Conventional Risk Scores for Predicting Major Adverse Cardiovascular Cerebrovascular Events After Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Systematic Review and Meta-Analysis

Comparing the Performance of Machine Learning Models and Conventional Risk Scores for Predicting Major Adverse Cardiovascular Cerebrovascular Events After Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Systematic Review and Meta-Analysis

The institutional review board of Chung-Ang University (number 1041078‐20240611-HR-144) approved the study protocol. We identified 75,122 records through a comprehensive search strategy, as presented in the PRISMA 2020 flowchart (Figure 1). After removing 30,547 duplicate records, 44,575 records remained for screening. Of these, 16 were excluded as they had been retracted due to issues such as ethical concerns or compromised data integrity.

Min-Young Yu, Hae Young Yoo, Ga In Han, Eun-Jung Kim, Youn-Jung Son

J Med Internet Res 2025;27:e76215