Showing 41 - 60 results of 519 for search '"Xiamen"', query time: 0.05s Refine Results
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    Factors Associated With Reflective Practice Among Specialist Nurses in China: A Latent Profile Analysis by Lichun Xu, Liyu Lin, Aixuan Guan, Qingqing Wang, Feng Lin, Weicong Lin, Jing Li

    Published 2024-12-01
    “…Methods A General Information Questionnaire and a Reflective Practice Questionnaire were distributed from July to August 2023 to specialist nurses who had obtained a Certificate of Nursing Practice, completed systematic specialist nurse training and were professionally qualified in six tertiary general hospitals in Xiamen, China. Results A total of 344 specialist nurses participated in the study. …”
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    Efficacy and safety of neoadjuvant therapy with tislelizumab plus axitinib for nonmetastatic renal cell carcinoma with inferior vena cava tumor thrombus: a retrospective study by Zhongjie Zhao, Zhengsheng Liu, Kaiyan Zhang, Wei Li, Lijian Zhang, Bingliang Jiang, Bin Chen, Jinchun Xing, Xuegang Wang

    Published 2025-01-01
    “…In this retrospective study, seven patients of nonmetastatic RCC with IVC tumor thrombus who received 3 cycles of neoadjuvant therapy with tislelizumab plus axitinib at the First Affiliated Hospital of Xiamen University from May 2020 to December 2023 were included. …”
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    Integrating retrieval-augmented generation for enhanced personalized physician recommendations in web-based medical services: model development study by Yingbin Zheng, Yiwei Yan, Sai Chen, Yunping Cai, Kun Ren, Yishan Liu, Jiaying Zhuang, Min Zhao

    Published 2025-01-01
    “…However, providing personalized physician recommendations remains a challenge, often relying on manual triage by schedulers, which can be limited by scalability and availability.ObjectiveThis study aimed to develop and validate a Retrieval-Augmented Generation-Based Physician Recommendation (RAGPR) model for better triage performance.MethodsThis study utilizes a comprehensive dataset consisting of 646,383 consultation records from the Internet Hospital of the First Affiliated Hospital of Xiamen University. The research primarily evaluates the performance of various embedding models, including FastText, SBERT, and OpenAI, for the purposes of clustering and classifying medical condition labels. …”
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