{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T20:09:45Z","timestamp":1783973385258,"version":"3.55.0"},"reference-count":48,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T00:00:00Z","timestamp":1771977600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003710","name":"Korea Health Industry Development Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003710","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Korea Ministry of Science and ICT","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010418","name":"IITP","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","thelancet.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["eBioMedicine"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1016\/j.ebiom.2026.106206","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T17:37:50Z","timestamp":1773423470000},"page":"106206","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Accurate, fair, and generalisable scaling of injury severity score-based AI with demographics in terms of mortality in patients with trauma: multi-centre, multi-national retrospective cohort study"],"prefix":"10.1016","volume":"126","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-4732-3396","authenticated-orcid":false,"given":"Yunjeong","family":"Choi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junepill","family":"Seok","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Young-Chul Oh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeremy","family":"Hsu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Do Wan","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Byungchul","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jayun","family":"Cho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Woocheol","family":"Jang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4661-7905","authenticated-orcid":false,"given":"Jina","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Na-eun","family":"Oh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8937-4368","authenticated-orcid":false,"given":"Jehyeuk","family":"Ahn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert J.","family":"Femia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul A.","family":"Testa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong Keon","family":"Yon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel K.","family":"Sodickson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0136-4458","authenticated-orcid":false,"given":"Wu Seong","family":"Kang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinseok","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"4","key":"10.1016\/j.ebiom.2026.106206_bib1","doi-asserted-by":"crossref","first-page":"523","DOI":"10.2105\/AJPH.90.4.523","article-title":"The global burden of injuries","volume":"90","author":"Krug","year":"2000","journal-title":"Am J Public Health"},{"issue":"8","key":"10.1016\/j.ebiom.2026.106206_bib2","doi-asserted-by":"crossref","first-page":"2293","DOI":"10.1097\/JS9.0000000000000481","article-title":"Trauma system establishment and outcome improvement: a retrospective national cohort study in South Korea","volume":"109","author":"Kwon","year":"2023","journal-title":"Int J Surg Lond Engl"},{"issue":"3","key":"10.1016\/j.ebiom.2026.106206_bib3","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1097\/00005373-197403000-00001","article-title":"The injury severity score: a method for describing patients with multiple injuries and evaluating emergency care","volume":"14","author":"Baker","year":"1974","journal-title":"J Trauma"},{"issue":"1","key":"10.1016\/j.ebiom.2026.106206_bib4","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1097\/00005373-198501000-00010","article-title":"Abbreviated Injury Scale and Injury Severity Score: a scoring chart","volume":"25","author":"Greenspan","year":"1985","journal-title":"J Trauma"},{"issue":"Suppl 3","key":"10.1016\/j.ebiom.2026.106206_bib5","doi-asserted-by":"crossref","first-page":"S64","DOI":"10.1016\/j.injury.2014.08.020","article-title":"How to define severely injured patients? -- an Injury Severity Score (ISS) based approach alone is not sufficient","volume":"45","author":"Paffrath","year":"2014","journal-title":"Injury"},{"issue":"1","key":"10.1016\/j.ebiom.2026.106206_bib6","doi-asserted-by":"crossref","first-page":"29","DOI":"10.4174\/astr.2018.95.1.29","article-title":"Pancreatic fistula and mortality after surgical management of pancreatic trauma: analysis of 81 consecutive patients during 11 years at a Korean trauma center","volume":"95","author":"Kang","year":"2018","journal-title":"Ann Surg Treat Res"},{"issue":"2","key":"10.1016\/j.ebiom.2026.106206_bib7","doi-asserted-by":"crossref","first-page":"114","DOI":"10.20408\/jti.2023.0087","article-title":"Validation of chest trauma scoring systems in polytrauma: a retrospective study with 1,038 patients in Korea","volume":"37","author":"Kim","year":"2024","journal-title":"J Trauma Inj"},{"issue":"1","key":"10.1016\/j.ebiom.2026.106206_bib8","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-021-03024-1","article-title":"Artificial intelligence to predict in-hospital mortality using novel anatomical injury score","volume":"11","author":"Kang","year":"2021","journal-title":"Sci Rep"},{"key":"10.1016\/j.ebiom.2026.106206_bib9","author":"Kheya"},{"key":"10.1016\/j.ebiom.2026.106206_bib10","doi-asserted-by":"crossref","DOI":"10.2196\/53564","article-title":"Strategies to mitigate age-related bias in machine learning: scoping review","volume":"7","author":"Chu","year":"2024","journal-title":"JMIR Aging"},{"key":"10.1016\/j.ebiom.2026.106206_bib11","author":"Collins"},{"issue":"1","key":"10.1016\/j.ebiom.2026.106206_bib12","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1002\/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3","article-title":"Index for rating diagnostic tests","volume":"3","author":"Youden","year":"1950","journal-title":"Cancer"},{"key":"10.1016\/j.ebiom.2026.106206_bib13","doi-asserted-by":"crossref","first-page":"4209","DOI":"10.1038\/s41598-022-07939-1","article-title":"A clarification of the nuances in the fairness metrics landscape","volume":"12","author":"Castelnovo","year":"2022","journal-title":"Sci Rep"},{"issue":"18","key":"10.1016\/j.ebiom.2026.106206_bib14","doi-asserted-by":"crossref","first-page":"3054","DOI":"10.3390\/rs12183054","article-title":"On the importance of train\u2013test split ratio of datasets in automatic landslide detection by supervised classification","volume":"12","author":"Pawluszek-Filipiak","year":"2020","journal-title":"Remote Sens"},{"key":"10.1016\/j.ebiom.2026.106206_bib15","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J Artif Intell Res"},{"issue":"9","key":"10.1016\/j.ebiom.2026.106206_bib16","doi-asserted-by":"crossref","first-page":"826","DOI":"10.1016\/S0895-4356(03)00207-5","article-title":"External validation is necessary in prediction research: a clinical example","volume":"56","author":"Bleeker","year":"2003","journal-title":"J Clin Epidemiol"},{"key":"10.1016\/j.ebiom.2026.106206_bib17","series-title":"Breakthroughs in Statistics","first-page":"569","article-title":"Bootstrap methods: another look at the jackknife","author":"Efron","year":"1992"},{"key":"10.1016\/j.ebiom.2026.106206_bib18","series-title":"Empirical Inference","first-page":"37","article-title":"Explaining AdaBoost","author":"Schapire","year":"2013"},{"issue":"2","key":"10.1016\/j.ebiom.2026.106206_bib19","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/s11749-016-0481-7","article-title":"A random forest guided tour","volume":"25","author":"Biau","year":"2016","journal-title":"TEST"},{"key":"10.1016\/j.ebiom.2026.106206_bib20","series-title":"Frontiers in neurorobotics","author":"Natekin","year":"2013"},{"key":"10.1016\/j.ebiom.2026.106206_bib21","article-title":"Lightgbm: a highly efficient gradient boosting decision tree","volume":"30","author":"Ke","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"10.1016\/j.ebiom.2026.106206_bib22","series-title":"Proceedings of the 22nd acm Sigkdd International Conference on Knowledge Discovery and Data Mining","first-page":"785","article-title":"Xgboost: a scalable tree boosting system","author":"Chen","year":"2016"},{"issue":"18","key":"10.1016\/j.ebiom.2026.106206_bib23","doi-asserted-by":"crossref","first-page":"2395","DOI":"10.1161\/CIRCULATIONAHA.106.682658","article-title":"Logistic regression","volume":"117","author":"LaValley","year":"2008","journal-title":"Circulation"},{"issue":"2","key":"10.1016\/j.ebiom.2026.106206_bib24","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s11222-009-9153-8","article-title":"Estimation of prediction error by using K-fold cross-validation","volume":"21","author":"Fushiki","year":"2011","journal-title":"Stat Comput"},{"key":"10.1016\/j.ebiom.2026.106206_bib25","doi-asserted-by":"crossref","first-page":"837","DOI":"10.2307\/2531595","article-title":"Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach","volume":"44","author":"DeLong","year":"1988","journal-title":"Biometrics"},{"key":"10.1016\/j.ebiom.2026.106206_bib26","author":"Bertail"},{"key":"10.1016\/j.ebiom.2026.106206_bib27","author":"Lundberg"},{"issue":"1","key":"10.1016\/j.ebiom.2026.106206_bib28","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1186\/s12916-019-1466-7","article-title":"Calibration: the Achilles heel of predictive analytics","volume":"17","author":"Van Calster","year":"2019","journal-title":"BMC Med"},{"key":"10.1016\/j.ebiom.2026.106206_bib29","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s41512-019-0064-7","article-title":"A simple, step-by-step guide to interpreting decision curve analysis","volume":"3","author":"Vickers","year":"2019","journal-title":"Diagn Progn Res"},{"key":"10.1016\/j.ebiom.2026.106206_bib30","series-title":"Regression Modeling Strategies","first-page":"475","article-title":"Cox proportional hazards regression model","author":"Harrell","year":"2015"},{"key":"10.1016\/j.ebiom.2026.106206_bib31","series-title":"Proceedings of the 22nd international conference on Machine learning - ICML \u201905","first-page":"625","article-title":"Predicting good probabilities with supervised learning","author":"Niculescu-Mizil","year":"2005"},{"key":"10.1016\/j.ebiom.2026.106206_bib32","series-title":"Machine learning and knowledge discovery in databases","first-page":"103","article-title":"Non-parametric bayesian isotonic calibration: fighting over-confidence in binary classification","author":"Allikivi","year":"2020"},{"issue":"1","key":"10.1016\/j.ebiom.2026.106206_bib33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41746-025-01503-7","article-title":"Bias recognition and mitigation strategies in artificial intelligence healthcare applications","volume":"8","author":"Hasanzadeh","year":"2025","journal-title":"NPJ Digit Med"},{"issue":"1","key":"10.1016\/j.ebiom.2026.106206_bib34","doi-asserted-by":"crossref","first-page":"1100","DOI":"10.1038\/s41598-025-85420-5","article-title":"External validation of an artificial intelligence model using clinical variables, including ICD-10 codes, for predicting in-hospital mortality among trauma patients: a multicenter retrospective cohort study","volume":"15","author":"Lee","year":"2025","journal-title":"Sci Rep"},{"key":"10.1016\/j.ebiom.2026.106206_bib35","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1186\/1757-7241-20-63","article-title":"Systematic review of predictive performance of injury severity scoring tools","volume":"20","author":"Tohira","year":"2012","journal-title":"Scand J Trauma Resusc Emerg Med"},{"key":"10.1016\/j.ebiom.2026.106206_bib36","doi-asserted-by":"crossref","DOI":"10.3389\/fsurg.2022.924810","article-title":"Decision support by machine learning systems for acute management of severely injured patients: a systematic review","volume":"9","author":"Baur","year":"2022","journal-title":"Front Surg"},{"issue":"12","key":"10.1016\/j.ebiom.2026.106206_bib37","doi-asserted-by":"crossref","DOI":"10.2196\/43757","article-title":"Model for predicting In-Hospital mortality of physical trauma patients using artificial intelligence techniques: nationwide population-based study in Korea","volume":"24","author":"Lee","year":"2022","journal-title":"J Med Internet Res"},{"key":"10.1016\/j.ebiom.2026.106206_bib38","doi-asserted-by":"crossref","DOI":"10.2196\/49283","article-title":"An artificial intelligence model for predicting trauma mortality among emergency department patients in South Korea: retrospective cohort study","volume":"25","author":"Lee","year":"2023","journal-title":"J Med Internet Res"},{"issue":"6","key":"10.1016\/j.ebiom.2026.106206_bib39","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2022.16393","article-title":"A prehospital triage system to detect traumatic intracranial hemorrhage using machine learning algorithms","volume":"5","author":"Abe","year":"2022","journal-title":"JAMA Netw Open"},{"issue":"10","key":"10.1016\/j.ebiom.2026.106206_bib40","doi-asserted-by":"crossref","first-page":"1088","DOI":"10.1001\/jamasurg.2023.2293","article-title":"Development of a machine learning\u2013based prescriptive tool to address racial disparities in access to care after penetrating trauma","volume":"158","author":"Gebran","year":"2023","journal-title":"JAMA Surg"},{"issue":"11","key":"10.1016\/j.ebiom.2026.106206_bib41","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0242166","article-title":"Using the National Trauma Data Bank (NTDB) and machine learning to predict trauma patient mortality at admission","volume":"15","author":"Tsiklidis","year":"2020","journal-title":"PloS One"},{"issue":"1","key":"10.1016\/j.ebiom.2026.106206_bib42","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1097\/TA.0000000000003158","article-title":"Trauma outcome predictor: an artificial intelligence interactive smartphone tool to predict outcomes in trauma patients","volume":"91","author":"Maurer","year":"2021","journal-title":"J Trauma Acute Care Surg"},{"issue":"1","key":"10.1016\/j.ebiom.2026.106206_bib43","doi-asserted-by":"crossref","first-page":"5176","DOI":"10.1038\/s41598-023-32453-3","article-title":"Assessing the utility of a sliding-windows deep neural network approach for risk prediction of trauma patients","volume":"13","author":"Bonde","year":"2023","journal-title":"Sci Rep"},{"issue":"10","key":"10.1016\/j.ebiom.2026.106206_bib44","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0205836","article-title":"Validation of deep-learning-based triage and acuity score using a large national dataset","volume":"13","author":"Kwon","year":"2018","journal-title":"PloS One"},{"key":"10.1016\/j.ebiom.2026.106206_bib45","author":"Grinsztajn"},{"issue":"1","key":"10.1016\/j.ebiom.2026.106206_bib46","doi-asserted-by":"crossref","DOI":"10.1038\/srep26094","article-title":"Deep patient: an unsupervised representation to predict the future of patients from the electronic health records","volume":"6","author":"Miotto","year":"2016","journal-title":"Sci Rep"},{"key":"10.1016\/j.ebiom.2026.106206_bib47","series-title":"Proceedings of the 24th International Conference on Enterprise Information Systems","first-page":"602","article-title":"A performance analysis of classifiers on imbalanced data","author":"Garcia","year":"2022"},{"key":"10.1016\/j.ebiom.2026.106206_bib48","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.1038\/s41467-025-68198-y","article-title":"Prehospital real-time AI for trauma mortality prediction: a multi-institutional and multi-national validation study","volume":"17","author":"Oh","year":"2026","journal-title":"Nat Commun"}],"container-title":["eBioMedicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2352396426000885?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2352396426000885?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T19:59:29Z","timestamp":1783972769000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2352396426000885"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":48,"alternative-id":["S2352396426000885"],"URL":"https:\/\/doi.org\/10.1016\/j.ebiom.2026.106206","relation":{},"ISSN":["2352-3964"],"issn-type":[{"value":"2352-3964","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Accurate, fair, and generalisable scaling of injury severity score-based AI with demographics in terms of mortality in patients with trauma: multi-centre, multi-national retrospective cohort study","name":"articletitle","label":"Article Title"},{"value":"eBioMedicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ebiom.2026.106206","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"106206"}}