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Shouyin Jiang on Muck Rack

Shouyin Jiang

As seen in: Frontiers, Frontiers in Medicine, Frontiers in Nutrition, Frontiers in Public Health
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Articles

Enteral nutrition interruption in critically ill patients: a systematic review and meta-analysis of prevalence, causes, and clinical outcomes

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By Xiaojing Guo, Libing Jiang, Shouyin Jiang, Xiaoxia Huang, Jiaying Tang, Xiuqin Feng, Shanxiang Xu
| Frontiers in Nutrition
Abstract Introduction: Enteral nutrition interruption (ENI) is common in critically ill patients and may compromise nutritional delivery, but its prevalence, causes, and clinical consequences remain uncertain. This systematic review and meta-analysis synthesized evidence on ENI prevalence, contributing factors, clinical outcomes, and potential interventions. Methods: PubMed, Embase, CINAHL, Web of Science, Cochrane Library, CNKI, and WanFang Data were searched from inception through December 2025.
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Research progress in agitation early warning monitoring and new technologies for critically ill patients: a narrative review

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By Jintao Wei, Shouyin Jiang, Mengting Yan, qiang li, Pengyuan Chen, Shanxiang Xu, Mao Zhang, Zhiying Kang
| Frontiers in Medicine
Abstract Agitation is a common and clinically significant problem in critically ill patients, closely associated with unplanned extubation, device removal, prolonged hospital stay, and increased mortality. Traditional assessment tools such as the richmond agitation-sedation scale (RASS) and sedation-agitation scale (SAS), despite their widespread use, are limited by intermittent assessment, subjectivity, and inability to provide continuous monitoring.
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Performance evaluation of large language models in the diagnosis of emergency internal medicine diseases: a retrospective study

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By Jintao Wei, Shouyin Jiang, Ting Yin, qiang li, Yu Tian, Mengting Yan, Ximei Ma, Shanxiang Xu, Mao Zhang, Jiangchun Ma, Zhuyi Shen, Xiangkang Lv
| Frontiers in Public Health Verified
Abstract Objective: Medical domain large language models (LLMs) exhibit verified clinical decision-support capabilities in simulated case analyses and standardized tests, yet their diagnostic efficacy in real-world emergency settings remain insufficiently explored. This study evaluates the diagnostic performance of 5 mainstream LLMs (ChatGPT-4o, Gemini-2.0, Grok3, DeepSeek-V3, Doubao) against emergency department junior physicians (EDJP) on real-world emergency internal medicine cases.
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