{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["55"],"submitter":["Gauss T"],"pubmed_abstract":["Background
Machine learning could improve the timely identification of trauma patients in need of hemorrhage control resuscitation (HCR), but the real-life performance remains unknown. The ShockMatrix study aimed to compare the predictive performance of a machine learning algorithm with that of clinicians in identifying the need for HCR.Methods
Prospective, observational study in eight level-1 trauma centers. Upon receiving a prealert call, trauma clinicians in the resuscitation room entered nine predictor variables into a dedicated smartphone app and provided a subjective prediction of the need for HCR. These predictors matched those used in the machine learning model. The primary outcome, need for HCR, was defined as: transfusion in the resuscitation room, transfusion of "],"journal":["The Lancet regional health. Europe"],"pagination":["101340"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12205608"],"repository":["biostudies-literature"],"pubmed_title":["Comparison of machine learning and human prediction to identify trauma patients in need of hemorrhage control resuscitation (ShockMatrix study): a prospective observational study."],"pmcid":["PMC12205608"],"pubmed_authors":["Rotival J","James A","Christophe Q","Geeraerts T","Vilotitch A","Mathieu R","Anatole H","Olivier D","Nadal JP","Gosset P","Brieu B","Salah S","Gettes S","Bouillon JB","Audibert G","Meaudre E","de Cherisey H","Bouzat P","Leone M","Abback PS","Morel J","Caroline J","Holleville M","Gay S","Kallel H","Escudier E","Floch T","Gaertner E","Josse J","Montalescaut E","Collard C","Alexandre B","Scotto M","Legros V","Higel N","Medjkoune S","Moyer JD","Nathalie D","Mathieu B","Ramonda V","Clavier T","Meyer A","Traumabase Group","Hammad E","Yordanov Y","Anne G","Gauss T","David JS","Lukaszewicz AC","Mermillod Blondin R","Hanouz JL","Julien P","Bouhours G","Cesareo E","Jean FX","Lepetre P","Popoff B","Willig M","Bounes F","Perez P","Werner M","Malec L","Jaillette C","Delphine G","Mellati N","Lefrancois V","Fremery A","Bijok B","Delhaye N","Pujo J","Cohen B","Dussau L","Jean P","Duclos G","Colas C"],"additional_accession":[]},"is_claimable":false,"name":"Comparison of machine learning and human prediction to identify trauma patients in need of hemorrhage control resuscitation (ShockMatrix study): a prospective observational study.","description":"Background
Machine learning could improve the timely identification of trauma patients in need of hemorrhage control resuscitation (HCR), but the real-life performance remains unknown. The ShockMatrix study aimed to compare the predictive performance of a machine learning algorithm with that of clinicians in identifying the need for HCR.Methods
Prospective, observational study in eight level-1 trauma centers. Upon receiving a prealert call, trauma clinicians in the resuscitation room entered nine predictor variables into a dedicated smartphone app and provided a subjective prediction of the need for HCR. These predictors matched those used in the machine learning model. The primary outcome, need for HCR, was defined as: transfusion in the resuscitation room, transfusion of ","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Aug","modification":"2026-06-03T07:37:35.413Z","creation":"2026-04-26T03:10:15.573Z"},"accession":"S-EPMC12205608","cross_references":{"pubmed":["40584589"],"doi":["10.1016/j.lanepe.2025.101340"]}}