{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Xiong S"],"funding":["Top Talent Support Program for young and middle-aged people of Wuxi Health Committee","Wuxi Municipal Bureau on Science and Technology","Wuxi Taihu Lake Talent Plan","Natural Science Foundation of Jiangsu Province"],"pagination":["746"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12492913"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["25(1)"],"pubmed_abstract":["<h4>Background</h4>Owing to the absence of straightforward and scientifically validated screening and evaluation tools for the timely identification, diagnosis, and treatment of critical influenza A infection in children, this study aimed to construct an effective model for the early identification of patients at high risk of progressing to critical influenza A infection.<h4>Methods</h4>The prediction model was developed using the registration data of children diagnosed with influenza A who were admitted to Wuxi Children's Hospital, the Children's Hospital Affiliated to Soochow University and the Children's Hospital Affiliated to Fudan University. Patients were randomly divided into a training group and a validation group at a 7:3 ratio. A logistic regression model was established based on"],"journal":["BMC pediatrics"],"pubmed_title":["A simple, rapid, and cost-effective model for predicting critical influenza a infection in children: a multicentre, retrospective cohort study."],"pmcid":["PMC12492913"],"funding_grant_id":["BK20230189","K20221033","DJTD202304","BJ2023089"],"pubmed_authors":["Zhu P","Qian J","Jiang L","Guo Y","Chen W","Xiong S","Bai Z","Wang Y","Li L"],"additional_accession":[]},"is_claimable":false,"name":"A simple, rapid, and cost-effective model for predicting critical influenza a infection in children: a multicentre, retrospective cohort study.","description":"<h4>Background</h4>Owing to the absence of straightforward and scientifically validated screening and evaluation tools for the timely identification, diagnosis, and treatment of critical influenza A infection in children, this study aimed to construct an effective model for the early identification of patients at high risk of progressing to critical influenza A infection.<h4>Methods</h4>The prediction model was developed using the registration data of children diagnosed with influenza A who were admitted to Wuxi Children's Hospital, the Children's Hospital Affiliated to Soochow University and the Children's Hospital Affiliated to Fudan University. Patients were randomly divided into a training group and a validation group at a 7:3 ratio. A logistic regression model was established based on","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Oct","modification":"2026-06-04T02:34:21.388Z","creation":"2026-05-31T03:06:29.47Z"},"accession":"S-EPMC12492913","cross_references":{"pubmed":["41039296"],"doi":["10.1186/s12887-025-06085-7"]}}