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Evaluating the Characteristics and Outcomes of Acute Pharmaceutical Exposure in Children: 5-Year Retrospective Study

Evaluating the Characteristics and Outcomes of Acute Pharmaceutical Exposure in Children: 5-Year Retrospective Study

The study also found that almost all asymptomatic children had normal laboratory results, consistent with Wang et al’s [35] study in the United States, which found no positive results in extensive screening and electrocardiography tests for asymptomatic children aged 12 years or younger. Therefore, the necessity of these tests in asymptomatic children with accidental drug overdoses remains debatable.

Zhu Yan Duan, Yan Ning Qu, Rui Tang, Jun Ting Liu, Hui Wang, Meng Yi Sheng, Liang Liang Wang, Shuang Liu, Jiao Li, Lin Ying Guo, Si Zheng

JMIR Pediatr Parent 2025;8:e66951

Imaging-Based AI for Predicting Lymphovascular Space Invasion in Cervical Cancer: Systematic Review and Meta-Analysis

Imaging-Based AI for Predicting Lymphovascular Space Invasion in Cervical Cancer: Systematic Review and Meta-Analysis

In contrast, other studies, such as Li et al [19] and Wang et al [20], observed considerably lower performance, with AUC values of 0.72 and 0.73, respectively. These discrepancies can be attributed to factors such as data quality, sample size, and model architecture. Low-quality datasets, such as retrospective studies or single-center studies, may introduce selection bias and limit the generalizability of models, thereby affecting the reliability of radiomics approaches in clinical practice [21].

Lizhen She, Yunfeng Li, Hongyong Wang, Jun Zhang, Yuechen Zhao, Jie Cui, Ling Qiu

J Med Internet Res 2025;27:e71091

Prediction of Insulin Resistance in Nondiabetic Population Using LightGBM and Cohort Validation of Its Clinical Value: Cross-Sectional and Retrospective Cohort Study

Prediction of Insulin Resistance in Nondiabetic Population Using LightGBM and Cohort Validation of Its Clinical Value: Cross-Sectional and Retrospective Cohort Study

The vertical coordinate (y-axis) shows the top 20 features and (B) shows the explanation of each feature impact on insulin resistance in the prediction model by the Shapley Additive Explanations (SHAP) values in the Light Gradient Boosting Machine algorithm. A/G: albumin/globulin ratio; ALT: alanine aminotransferase; Cr: creatinine; FBG: fasting blood glucose; HDL-C: high-density lipoprotein cholesterol; SG: serum glutamic; TBA: bile acids; TBIL: total bilirubin; TG: triglycerides; UA: uric acid.

Ting Peng, Rujia Miao, Hao Xiong, Yanhui Lin, Duzhen Fan, Jiayi Ren, Jiangang Wang, Yuan Li, Jianwen Chen

JMIR Med Inform 2025;13:e72238