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Cited by in CrossRef
For: Kataria S, Singh O, Juneja D, Goel A, Bhide M, Yadav D. Hypoperfusion context as a predictor of 28-d all-cause mortality in septic shock patients: A comparative observational study. World J Clin Cases 2023; 11(16): 3765-3779 [PMID: 37383132 DOI: 10.12998/wjcc.v11.i16.3765]
URL: https://www.wjgnet.com/2307-8960/full/v11/i16/3765.htm
Number Citing Articles
1
Kavous Shahsavarinia, Tara Sabzevari, Kamran Shadvar, Seied Hadi Saghaleini, Ata Mahmoodpoor, Aliakbar Ghamari, Amir Vahedian-Azimi, Abbas Samim, Farshid Rahimi-Bashar. Comparison of Predictive Ability of Macrocirculation and Microcirculation Markers on Outcomes of Patients with Septic Shock During Initial Fluid Resuscitation: A Prospective Observational StudyIntensive Care Research 2024; 4(1): 38 doi: 10.1007/s44231-024-00059-6
2
Kangping Hui, Chengying Hong, Yihan Xiong, Jinquan Xia, Wei Huang, Andi Xia, Shunyao Xu, Yuting Chen, Zhongwei Zhang, Huaisheng Chen. LASSO-Based Machine Learning Algorithm for Prediction of PICS Associated with SepsisInfection and Drug Resistance 2024; : 2701 doi: 10.2147/IDR.S464906
3
Nasrin Nikravangolsefid, Swetha Reddy, Hong Hieu Truong, Mariam Charkviani, Jacob Ninan, Larry J. Prokop, Supawadee Suppadungsuk, Waryaam Singh, Kianoush B. Kashani, Juan Pablo Domecq Garces. Machine learning for predicting mortality in adult critically ill patients with Sepsis: A systematic reviewJournal of Critical Care 2024; 84: 154889 doi: 10.1016/j.jcrc.2024.154889