{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T13:07:14Z","timestamp":1787317634289,"version":"build-2736575974"},"reference-count":29,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T00:00:00Z","timestamp":1784073600000},"content-version":"am","delay-in-days":317,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100000050","name":"National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017540","name":"National Heart Lung and Blood Institute Division of Intramural Research","doi-asserted-by":"publisher","award":["T32HL139430"],"award-info":[{"award-number":["T32HL139430"]}],"id":[{"id":"10.13039\/100017540","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","journalofsurgicalresearch.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Surgical Research"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1016\/j.jss.2025.06.040","type":"journal-article","created":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T23:38:36Z","timestamp":1752622716000},"page":"153-160","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["Machine Learning Predicts Mortality and Respiratory Failure in Patients Admitted With Rib Fractures"],"prefix":"10.1016","volume":"313","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2687-2380","authenticated-orcid":false,"given":"Travis J.","family":"Miles","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jose","family":"Mendez-Reyes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ava K.","family":"Mokhtari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James W.","family":"Suliburk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chad T.","family":"Wilson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin D.","family":"Zielinski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ravi K.","family":"Ghanta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jss.2025.06.040_bib1","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1097\/00005373-199001000-00015","article-title":"Mortality prognostic factors in chest injury","volume":"30","author":"Gaillard","year":"1990","journal-title":"J Trauma"},{"key":"10.1016\/j.jss.2025.06.040_bib2","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1177\/000313480807400406","article-title":"Perils of rib fractures","volume":"74","author":"Sharma","year":"2008","journal-title":"Am Surg"},{"key":"10.1016\/j.jss.2025.06.040_bib3","doi-asserted-by":"crossref","first-page":"1040","DOI":"10.1097\/00005373-200006000-00007","article-title":"Rib fractures in the elderly","volume":"48","author":"Bulger","year":"2000","journal-title":"J Trauma"},{"key":"10.1016\/j.jss.2025.06.040_bib4","doi-asserted-by":"crossref","first-page":"2180","DOI":"10.1016\/j.injury.2021.05.027","article-title":"The financial burden of rib fractures: National estimates 2007 to 2016","volume":"52","author":"Sarode","year":"2021","journal-title":"Injury"},{"key":"10.1016\/j.jss.2025.06.040_bib5","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1097\/TA.0000000000001350","article-title":"Operative fixation of rib fractures after blunt trauma: a practice management guideline from the Eastern Association for the Surgery of Trauma","volume":"82","author":"Kasotakis","year":"2017","journal-title":"J Trauma Acute Care Surg"},{"key":"10.1016\/j.jss.2025.06.040_bib6","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1097\/TA.0000000000001301","article-title":"Western trauma association critical decisions in trauma: management of rib fractures","volume":"82","author":"Brasel","year":"2017","journal-title":"J Trauma Acute Care Surg"},{"key":"10.1016\/j.jss.2025.06.040_bib7","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1016\/j.amjsurg.2006.08.048","article-title":"A multidisciplinary clinical pathway decreases rib fracture\u2013associated infectious morbidity and mortality in high-risk trauma patients","volume":"192","author":"Todd","year":"2006","journal-title":"Am J Surg"},{"key":"10.1016\/j.jss.2025.06.040_bib8","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1097\/TA.0000000000003866","article-title":"A novel, evidence-based, comprehensive clinical decision support system improves outcomes for patients with traumatic rib fractures","volume":"95","author":"Jones","year":"2023","journal-title":"J Trauma Acute Care Surg"},{"key":"10.1016\/j.jss.2025.06.040_bib9","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1016\/j.surg.2014.06.045","article-title":"A chest trauma scoring system to predict outcomes","volume":"156","author":"Chen","year":"2014","journal-title":"Surgery"},{"key":"10.1016\/j.jss.2025.06.040_bib10","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1097\/00005373-200009000-00018","article-title":"Appraisal of early evaluation of blunt chest trauma: development of a standardized scoring system for initial clinical decision making","volume":"49","author":"Pape","year":"2000","journal-title":"J Trauma"},{"key":"10.1016\/j.jss.2025.06.040_bib11","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1016\/j.amjsurg.2012.05.015","article-title":"Predicting outcome of patients with chest wall injury","volume":"204","author":"Pressley","year":"2012","journal-title":"Am J Surg"},{"key":"10.1016\/j.jss.2025.06.040_bib12","doi-asserted-by":"crossref","first-page":"1260","DOI":"10.1097\/TA.0000000000002480","article-title":"The Sequential Clinical Assessment of Respiratory Function (SCARF) score: a dynamic pulmonary physiologic score that predicts adverse outcomes in critically ill rib fracture patients","volume":"87","author":"Hardin","year":"2019","journal-title":"J Trauma Acute Care Surg"},{"key":"10.1016\/j.jss.2025.06.040_bib13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jss.2018.03.025","article-title":"Quantification of rib fractures by different scoring systems","volume":"229","author":"Fokin","year":"2018","journal-title":"J Surg Res"},{"key":"10.1016\/j.jss.2025.06.040_bib14","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.jss.2011.09.018","article-title":"Comparison of different thoracic trauma scoring systems in regards to prediction of post-traumatic complications and outcome in blunt chest trauma","volume":"176","author":"Mommsen","year":"2012","journal-title":"J Surg Res"},{"key":"10.1016\/j.jss.2025.06.040_bib15","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2023.22285","article-title":"Development and validation of a machine learning model to identify patients before surgery at high risk for postoperative adverse events","volume":"6","author":"Mahajan","year":"2023","journal-title":"JAMA Netw Open"},{"key":"10.1016\/j.jss.2025.06.040_bib16","doi-asserted-by":"crossref","first-page":"2075","DOI":"10.1016\/j.jtcvs.2020.07.105","article-title":"Machine learning improves mortality risk prediction after cardiac surgery: systematic review and meta-analysis","volume":"163","author":"Benedetto","year":"2022","journal-title":"J Thorac Cardiovasc Surg"},{"key":"10.1016\/j.jss.2025.06.040_bib17","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1001\/jamasurg.2018.0483","article-title":"Practical guide to surgical data sets: national trauma Data Bank (NTDB)","volume":"153","author":"Hashmi","year":"2018","journal-title":"JAMA Surg"},{"key":"10.1016\/j.jss.2025.06.040_bib18","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.jss.2024.11.046","article-title":"Clinical outcomes following component therapy only versus whole Blood plus component versus whole Blood only in geriatric trauma patients with isolated chest or abdominal injuries with or without traumatic brain injury","volume":"305","author":"Nasef","year":"2025","journal-title":"J Surg Res"},{"key":"10.1016\/j.jss.2025.06.040_bib19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41746-022-00625-6","article-title":"Multi-center validation of machine learning model for preoperative prediction of postoperative mortality","volume":"5","author":"Lee","year":"2022","journal-title":"Npj Digit Med"},{"key":"10.1016\/j.jss.2025.06.040_bib20","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pmed.1002701","article-title":"Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): a retrospective, single-site study","volume":"15","author":"Corey","year":"2018","journal-title":"PLoS Med"},{"key":"10.1016\/j.jss.2025.06.040_bib21","doi-asserted-by":"crossref","DOI":"10.1161\/JAHA.120.019697","article-title":"Supplementing existing societal risk models for surgical aortic valve replacement with machine learning for improved prediction","volume":"10","author":"Kilic","year":"2021","journal-title":"JAHA"},{"key":"10.1016\/j.jss.2025.06.040_bib22","article-title":"Interpretable machine learning for discovery: statistical challenges and opportunities","volume":"11","author":"Allen","year":"2024","journal-title":"Annu Rev Stat Its Appl"},{"key":"10.1016\/j.jss.2025.06.040_bib23","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1007\/s00068-019-01183-5","article-title":"Predictors of mortality in patients with rib fractures","volume":"47","author":"Marini","year":"2021","journal-title":"Eur J Trauma Emerg Surg"},{"key":"10.1016\/j.jss.2025.06.040_bib24","doi-asserted-by":"crossref","first-page":"268","DOI":"10.20408\/jti.2021.0096","article-title":"Outcomes after rib fractures: more complex than a single number","volume":"35","author":"Colling","year":"2022","journal-title":"J Trauma Inj"},{"key":"10.1016\/j.jss.2025.06.040_bib25","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/S1010-7940(03)00256-2","article-title":"A comprehensive analysis of traumatic rib fractures: morbidity, mortality and management","volume":"24","author":"Sirmali","year":"2003","journal-title":"Eur J Cardio Thoracic Surg"},{"key":"10.1016\/j.jss.2025.06.040_bib26","doi-asserted-by":"crossref","first-page":"1124","DOI":"10.1016\/j.athoracsur.2009.06.002","article-title":"Risk factors for 24-hour mortality after traumatic rib fractures owing to motor vehicle accidents: a nationwide population-based study","volume":"88","author":"Lien","year":"2009","journal-title":"Ann Thorac Surg"},{"key":"10.1016\/j.jss.2025.06.040_bib27","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1007\/s11764-023-01465-3","article-title":"Machine learning (ML) techniques to predict breast cancer in imbalanced datasets: a systematic review","volume":"19","author":"Ghavidel","year":"2023","journal-title":"J Cancer Surviv"},{"key":"10.1016\/j.jss.2025.06.040_bib28","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1097\/TA.0000000000003619","article-title":"Scalable deep learning algorithm to compute percent pulmonary contusion among patients with rib fractures","volume":"93","author":"Choi","year":"2022","journal-title":"J Trauma Acute Care Surg"},{"key":"10.1016\/j.jss.2025.06.040_bib29","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1097\/TA.0000000000003913","article-title":"FasterRib: a deep learning algorithm to automate identification and characterization of rib fractures on chest computed tomography scans","volume":"95","author":"Edamadaka","year":"2023","journal-title":"J Trauma Acute Care Surg"}],"container-title":["Journal of Surgical Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0022480425003750?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0022480425003750?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T00:46:42Z","timestamp":1756774002000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0022480425003750"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":29,"alternative-id":["S0022480425003750"],"URL":"https:\/\/doi.org\/10.1016\/j.jss.2025.06.040","relation":{},"ISSN":["0022-4804"],"issn-type":[{"value":"0022-4804","type":"print"}],"subject":[],"published":{"date-parts":[[2025,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Machine Learning Predicts Mortality and Respiratory Failure in Patients Admitted With Rib Fractures","name":"articletitle","label":"Article Title"},{"value":"Journal of Surgical Research","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jss.2025.06.040","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}]}}