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  • [\'MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, PR China.\', \'Hunan Provincial People\'s Hospital, Hunan Normal University, Changsha, PR China; Changsha Clinical Research Center for Kidney Disease, Changsha, PR China; Hunan Clinical Research Center for Chronic Kidney Disease, Changsha, PR China.\', \'Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, PR China.\', \'MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, PR China. Electronic address: guoshuixia75@163.com.\', \'School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, PR China. Electronic address: feiyukm@aliyun.com.\']
?:citedBy
  • -1
?:creator
?:doi
  • S0735-6757(20)30784-110.1016/j.ajem.2020.08.090
?:doi
?:hasPublicationType
?:journal
  • The American journal of emergency medicine
is ?:pmid of
?:pmid
?:pmid
  • 33046291
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  • -1.0
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  • -1
?:title
  • Identifying and quantifying robust risk factors for mortality in critically ill patients with COVID-19 using quantile regression.
?:type
?:year
  • 2021

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