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Classifying the Information Needs of Survivors of Domestic Violence in Online Health Communities Using Large Language Models: Prediction Model Development and Evaluation Study

Classifying the Information Needs of Survivors of Domestic Violence in Online Health Communities Using Large Language Models: Prediction Model Development and Evaluation Study

For example, Hui et al [27] identified that both emotional and informational needs from OHCs were extractable. However, our study only focused on the accuracy of the information needed. To deliver a holistic care approach for survivors of DV, cocreation and validation with concerned parties regarding the LLM prediction results regarding both informational and emotional needs could greatly improve the practicality, applicability, feasibility, and usefulness of our proposed model.

Shaowei Guan, Vivian Hui, Gregor Stiglic, Rose Eva Constantino, Young Ji Lee, Arkers Kwan Ching Wong

J Med Internet Res 2025;27:e65397

Diagnosis of Sarcopenia Using Convolutional Neural Network Models Based on Muscle Ultrasound Images: Prospective Multicenter Study

Diagnosis of Sarcopenia Using Convolutional Neural Network Models Based on Muscle Ultrasound Images: Prospective Multicenter Study

Tang et al [24] developed an ultrasound-derived muscle assessment system based on muscle thickness, handgrip strength, and gait speed. The system had an overall diagnostic sensitivity of 92.7% and a specificity of 91% for sarcopenia. These studies have proved the effectiveness of conventional ultrasound methods; however, they are time-consuming and performed in diverse protocols.

Zi-Tong Chen, Xiao-Long Li, Feng-Shan Jin, Yi-Lei Shi, Lei Zhang, Hao-Hao Yin, Yu-Li Zhu, Xin-Yi Tang, Xi-Yuan Lin, Bei-Lei Lu, Qun Wang, Li-Ping Sun, Xiao-Xiang Zhu, Li Qiu, Hui-Xiong Xu, Le-Hang Guo

J Med Internet Res 2025;27:e70545