{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T12:47:01Z","timestamp":1780577221956,"version":"3.54.1"},"reference-count":34,"publisher":"Elsevier BV","issue":"7","license":[{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100001765","name":"Musculoskeletal Transplant Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100001765","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004374","name":"Medtronic","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004374","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000104","name":"National Aeronautics and Space Administration","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","thespinejournalonline.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["The Spine Journal"],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1016\/j.spinee.2023.03.015","type":"journal-article","created":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T18:39:08Z","timestamp":1680719948000},"page":"997-1006","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":11,"title":["Accounting for age in prediction of discharge destination following elective lumbar fusion: a supervised machine learning approach"],"prefix":"10.1016","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9521-9181","authenticated-orcid":false,"given":"Andrew","family":"Cabrera","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5068-6635","authenticated-orcid":false,"given":"Alexander","family":"Bouterse","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Nelson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5832-5594","authenticated-orcid":false,"given":"Jacob","family":"Razzouk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7782-5762","authenticated-orcid":false,"given":"Omar","family":"Ramos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher M.","family":"Bono","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5304-1645","authenticated-orcid":false,"given":"Wayne","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olumide","family":"Danisa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.spinee.2023.03.015_bib0001","series-title":"Healthcare Cost and Utilization Project (HCUP) Statistical Briefs","article-title":"Trends in Operating Room Procedures in U.S. Hospitals, 2001\u20132011","author":"Weiss","year":"2006"},{"issue":"5","key":"10.1016\/j.spinee.2023.03.015_bib0002","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1097\/BRS.0000000000002822","article-title":"Trends in lumbar fusion procedure rates and associated hospital costs for degenerative spinal diseases in the United States, 2004 to 2015","volume":"44","author":"Martin","year":"2019","journal-title":"Spine (Phila Pa 1976)"},{"issue":"3","key":"10.1016\/j.spinee.2023.03.015_bib0003","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/j.spinee.2019.10.007","article-title":"Can a machine learning model accurately predict patient resource utilization following lumbar spinal fusion?","volume":"20","author":"Karnuta","year":"2020","journal-title":"Spine J"},{"issue":"8","key":"10.1016\/j.spinee.2023.03.015_bib0004","doi-asserted-by":"crossref","first-page":"1431","DOI":"10.1007\/s00590-014-1439-7","article-title":"Elderly pelvic fractures: the incidence is increasing and patient demographics can be used to predict the outcome","volume":"24","author":"Clement","year":"2014","journal-title":"Eur J Orthop Surg Traumatol"},{"issue":"10","key":"10.1016\/j.spinee.2023.03.015_bib0005","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1097\/BRS.0000000000001364","article-title":"Patient-rated outcomes of lumbar fusion in patients with degenerative disease of the lumbar spine: does age matter?","volume":"41","author":"Marbacher","year":"2016","journal-title":"Spine (Phila Pa 1976)"},{"key":"10.1016\/j.spinee.2023.03.015_bib0006","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.jocn.2021.07.006","article-title":"The effect of patient age on discharge destination and complications after lumbar spinal fusion","volume":"91","author":"Pennicooke","year":"2021","journal-title":"J Clin Neurosci"},{"key":"10.1016\/j.spinee.2023.03.015_bib0007","unstructured":"An all-payer view of hospital discharge to postacute care, 2013 #205. Available at: https:\/\/www.hcup-us.ahrq.gov\/reports\/statbriefs\/sb205-Hospital-Discharge-Postacute-Care.jsp. Accessed December 15, 2022"},{"issue":"2","key":"10.1016\/j.spinee.2023.03.015_bib0008","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1097\/01.JMQ.0000753240.14141.87","article-title":"Discharge disposition and clinical outcomes after spine surgery","volume":"37","author":"Minetos","year":"2022","journal-title":"Am J Med Qual"},{"key":"10.1016\/j.spinee.2023.03.015_bib0009","doi-asserted-by":"crossref","DOI":"10.1016\/j.clineuro.2021.106801","article-title":"Discharge destination influences risks of readmission and complications after lumbar spine surgery in severely disabled patients","volume":"207","author":"Park","year":"2021","journal-title":"Clin Neurol Neurosurg"},{"issue":"5","key":"10.1016\/j.spinee.2023.03.015_bib0010","doi-asserted-by":"crossref","first-page":"E6","DOI":"10.3171\/2018.8.FOCUS18340","article-title":"Development of machine learning algorithms for prediction of discharge disposition after elective inpatient surgery for lumbar degenerative disc disorders","volume":"45","author":"Karhade","year":"2018","journal-title":"Neurosurg Focus"},{"key":"10.1016\/j.spinee.2023.03.015_bib0011","first-page":"2825","article-title":"Scikit-learn: machine learning in python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"10.1016\/j.spinee.2023.03.015_bib0012","series-title":"Python 3 Reference Manual","author":"Van Rossum","year":"2009"},{"issue":"1","key":"10.1016\/j.spinee.2023.03.015_bib0013","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1093\/bioinformatics\/btr597","article-title":"MissForest\u2014non-parametric missing value imputation for mixed-type data","volume":"28","author":"Stekhoven","year":"2012","journal-title":"Bioinformatics"},{"key":"10.1016\/j.spinee.2023.03.015_bib0014","unstructured":"Hyperparameter optimization using Scikit-Learn | SpringerLink. Available at: https:\/\/link.springer.com\/chapter\/10.1007\/978-1-4842-6579-6_2. Accessed December 15, 2022"},{"issue":"9-10","key":"10.1016\/j.spinee.2023.03.015_bib0015","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1002\/ansa.202200018","article-title":"Cross-validated permutation feature importance considering correlation between features","volume":"3","author":"Kaneko","year":"2022","journal-title":"Anal Sci Adv"},{"key":"10.1016\/j.spinee.2023.03.015_bib0016","doi-asserted-by":"crossref","DOI":"10.1016\/j.imu.2019.100180","article-title":"A random forest based predictor for medical data classification using feature ranking","volume":"15","author":"Alam","year":"2019","journal-title":"Inform Med Unlocked"},{"issue":"3","key":"10.1016\/j.spinee.2023.03.015_bib0017","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.2021200126","article-title":"Magician's corner: 9. Performance metrics for machine learning models","volume":"3","author":"Erickson","year":"2021","journal-title":"Radiol Artif Intell"},{"key":"10.1016\/j.spinee.2023.03.015_bib0018","doi-asserted-by":"crossref","unstructured":"Ling, C. X., Huang, J., & Zhang, H. (2003). AUC: a better measure than accuracy in comparing learning algorithms. In Advances in Artificial Intelligence: 16th Conference of the Canadian Society for Computational Studies of Intelligence, AI 2003, Halifax, Canada, June 11\u201313, 2003, Proceedings 16 (pp. 329-341). Springer Berlin Heidelberg.","DOI":"10.1007\/3-540-44886-1_25"},{"issue":"3","key":"10.1016\/j.spinee.2023.03.015_bib0019","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MCSE.2007.55","article-title":"Matplotlib: a 2D graphics environment","volume":"9","author":"Hunter","year":"2007","journal-title":"Comput Sci Eng"},{"key":"10.1016\/j.spinee.2023.03.015_bib0020","doi-asserted-by":"crossref","first-page":"e139","DOI":"10.1016\/j.wneu.2018.09.147","article-title":"Discharge destination as a predictor of postoperative outcomes and readmission following posterior lumbar fusion","volume":"122","author":"Arrighi-Allisan","year":"2019","journal-title":"World Neurosurg"},{"issue":"19","key":"10.1016\/j.spinee.2023.03.015_bib0021","doi-asserted-by":"crossref","first-page":"1535","DOI":"10.1097\/BRS.0000000000001575","article-title":"Predictors of discharge destination after lumbar spine fusion surgery","volume":"41","author":"Aldebeyan","year":"2016","journal-title":"Spine (Phila Pa 1976)"},{"issue":"6","key":"10.1016\/j.spinee.2023.03.015_bib0022","doi-asserted-by":"crossref","first-page":"766","DOI":"10.3171\/2020.5.SPINE20410","article-title":"Drivers for nonhome discharge in a consecutive series of 1502 patients undergoing 1- or 2-level lumbar fusion","volume":"33","author":"Ogura","year":"2020","journal-title":"J Neurosurg Spine"},{"key":"10.1016\/j.spinee.2023.03.015_bib0023","doi-asserted-by":"crossref","first-page":"e206","DOI":"10.1016\/j.wneu.2018.06.155","article-title":"Drivers of cost in adult thoracolumbar spine deformity surgery","volume":"118","author":"Stephens","year":"2018","journal-title":"World Neurosurg"},{"key":"10.1016\/j.spinee.2023.03.015_bib0024","doi-asserted-by":"crossref","first-page":"106902","DOI":"10.1016\/j.clineuro.2021.106902","article-title":"Patient- and hospital-related risk factors for non-routine discharge after lumbar decompression and fusion for spondylolisthesis","volume":"209","author":"Elsamadicy","year":"2021","journal-title":"Clin Neurol Neurosurg"},{"issue":"10","key":"10.1016\/j.spinee.2023.03.015_bib0025","doi-asserted-by":"crossref","first-page":"1845","DOI":"10.1016\/j.spinee.2018.03.022","article-title":"Predictors of adverse discharge disposition in adult spinal deformity and associated costs","volume":"18","author":"Passias","year":"2018","journal-title":"Spine J"},{"key":"10.1016\/j.spinee.2023.03.015_bib0026","doi-asserted-by":"crossref","first-page":"e14","DOI":"10.1016\/j.wneu.2019.09.026","article-title":"Discharge disposition after anterior cervical discectomy and fusion","volume":"132","author":"Karhade","year":"2019","journal-title":"World Neurosurg"},{"issue":"4","key":"10.1016\/j.spinee.2023.03.015_bib0027","doi-asserted-by":"crossref","first-page":"523","DOI":"10.3171\/2019.9.SPINE19644","article-title":"Predictors of nonroutine discharge among patients undergoing surgery for grade I spondylolisthesis: insights from the quality outcomes database","volume":"32","author":"Mummaneni","year":"2019","journal-title":"J Neurosurg Spine"},{"key":"10.1016\/j.spinee.2023.03.015_bib0028","doi-asserted-by":"crossref","first-page":"e427","DOI":"10.1016\/j.wneu.2018.11.183","article-title":"Predictors of discharge disposition following laminectomy for intradural extramedullary spinal tumors","volume":"123","author":"Ahn","year":"2019","journal-title":"World Neurosurg"},{"key":"10.1016\/j.spinee.2023.03.015_bib0029","doi-asserted-by":"crossref","first-page":"105875","DOI":"10.1016\/j.clineuro.2020.105875","article-title":"Influence of gender on discharge disposition after spinal fusion for adult spine deformity correction","volume":"194","author":"Elsamadicy","year":"2020","journal-title":"Clin Neurol Neurosurg"},{"key":"10.1016\/j.spinee.2023.03.015_bib0030","doi-asserted-by":"crossref","first-page":"e574","DOI":"10.1016\/j.wneu.2018.07.213","article-title":"Predictive risk factors of nonhome discharge following elective posterior cervical fusion","volume":"119","author":"Ye","year":"2018","journal-title":"World Neurosurg"},{"issue":"6","key":"10.1016\/j.spinee.2023.03.015_bib0031","doi-asserted-by":"crossref","first-page":"1433","DOI":"10.1007\/s00586-019-05928-z","article-title":"Predicting discharge placement after elective surgery for lumbar spinal stenosis using machine learning methods","volume":"28","author":"Ogink","year":"2019","journal-title":"Eur Spine J"},{"issue":"16","key":"10.1016\/j.spinee.2023.03.015_bib0032","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1097\/BRS.0000000000003475","article-title":"Machine learning for predictive modeling of 90-day readmission, major medical complication, and discharge to a facility in patients undergoing long segment posterior lumbar spine fusion","volume":"45","author":"Jain","year":"2020","journal-title":"Spine"},{"issue":"2","key":"10.1016\/j.spinee.2023.03.015_bib0033","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.jamcollsurg.2009.10.021","article-title":"Missing data in the American college of surgeons national surgical quality improvement program are not missing at random: implications and potential impact on quality assessments","volume":"210","author":"Hamilton","year":"2010","journal-title":"J Am Coll Surg"},{"key":"10.1016\/j.spinee.2023.03.015_bib0034","first-page":"572","article-title":"Interpretation of machine learning predictions for patient outcomes in electronic health records","volume":"2019","author":"Cava","year":"2020","journal-title":"AMIA Annu Symp Proc"}],"container-title":["The Spine Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1529943023001493?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1529943023001493?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,26]],"date-time":"2025-10-26T21:37:58Z","timestamp":1761514678000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1529943023001493"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7]]},"references-count":34,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["S1529943023001493"],"URL":"https:\/\/doi.org\/10.1016\/j.spinee.2023.03.015","relation":{},"ISSN":["1529-9430"],"issn-type":[{"value":"1529-9430","type":"print"}],"subject":[],"published":{"date-parts":[[2023,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Accounting for age in prediction of discharge destination following elective lumbar fusion: a supervised machine learning approach","name":"articletitle","label":"Article Title"},{"value":"The Spine Journal","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.spinee.2023.03.015","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 Elsevier Inc. All rights reserved.","name":"copyright","label":"Copyright"}]}}