{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Xu Y"],"funding":["NIHR Birmingham ECMC","Nanocommons H2020-EU","National Institute for Health Research (NIHR)","Surgical Reconstruction and Microbiology Research Centre","MAESTRIA","MRC Heath Data Research UK"],"pagination":["176-183"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10894820"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["41(3)"],"pubmed_abstract":["Background
Major incidents (MIs) are an important cause of death and disability. Triage tools are crucial to identifying priority 1 (P1) patients-those needing time-critical, life-saving interventions. Existing expert opinion-derived tools have limited evidence supporting their use. This study employs machine learning (ML) to develop and validate models for novel primary and secondary triage tools.Methods
Adults (16+ years) from the UK Trauma Audit and Research Network (TARN) registry (January 2008-December 2017) served as surrogates for MI victims, with P1 patients identified using predefined criteria. The TARN database was split chronologically into model training and testing (70:30) datasets. Input variables included physiological parameters, age, mechanism and anatomica"],"journal":["Emergency medicine journal : EMJ"],"pubmed_title":["Triage in major incidents: development and external validation of novel machine learning-derived primary and secondary triage tools."],"pmcid":["PMC10894820"],"funding_grant_id":["n/a","HDRUK/CFC/01","731032","NIHR200248","965286"],"pubmed_authors":["Malik N","Foster M","Hodgetts T","Keene D","Bowley D","Vassallo J","Chernbumroong S","Lord J","Belli A","Xu Y","Gkoutos G"],"additional_accession":[]},"is_claimable":false,"name":"Triage in major incidents: development and external validation of novel machine learning-derived primary and secondary triage tools.","description":"Background
Major incidents (MIs) are an important cause of death and disability. Triage tools are crucial to identifying priority 1 (P1) patients-those needing time-critical, life-saving interventions. Existing expert opinion-derived tools have limited evidence supporting their use. This study employs machine learning (ML) to develop and validate models for novel primary and secondary triage tools.Methods
Adults (16+ years) from the UK Trauma Audit and Research Network (TARN) registry (January 2008-December 2017) served as surrogates for MI victims, with P1 patients identified using predefined criteria. The TARN database was split chronologically into model training and testing (70:30) datasets. Input variables included physiological parameters, age, mechanism and anatomica","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Feb","modification":"2026-07-15T01:53:25.617Z","creation":"2026-06-27T03:18:21.635Z"},"accession":"S-EPMC10894820","cross_references":{"pubmed":["37751994"],"doi":["10.1136/emermed-2022-212440"]}}