{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T21:04:17Z","timestamp":1791493457744,"version":"4.3.4"},"reference-count":44,"publisher":"Elsevier BV","issue":"8","license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016206","name":"Clinical and Translational Science Institute, University of California, Los Angeles","doi-asserted-by":"publisher","award":["UL1TR001881"],"award-info":[{"award-number":["UL1TR001881"]}],"id":[{"id":"10.13039\/100016206","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009452","name":"English Institute of Sport","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100009452","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100013015","name":"Cedars-Sinai Medical Center","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100013015","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":[[2025,8]]},"DOI":"10.1016\/j.spinee.2025.01.031","type":"journal-article","created":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T17:09:46Z","timestamp":1738256986000},"page":"1596-1607","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":9,"title":["Preoperative anemia is an unsuspecting driver of machine learning prediction of adverse outcomes after lumbar spinal fusion"],"prefix":"10.1016","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-7217-5152","authenticated-orcid":false,"given":"Attri","family":"Ghosh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philip J.","family":"Freda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shane","family":"Shahrestani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andre E.","family":"Boyke","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alena","family":"Orlenko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyunjun","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicholas","family":"Matsumoto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tayo","family":"Obafemi-Ajayi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jason H.","family":"Moore","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4334-7306","authenticated-orcid":false,"given":"Corey T.","family":"Walker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.spinee.2025.01.031_bib0001","doi-asserted-by":"crossref","first-page":"105","DOI":"10.4103\/0974-8237.161589","article-title":"Pain management following spinal surgeries: an appraisal of the available options","volume":"6","author":"Bajwa","year":"2015","journal-title":"J Cranioverteb Junct Spine"},{"key":"10.1016\/j.spinee.2025.01.031_bib0002","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1097\/BRS.0b013e31820cccfb","article-title":"Spinal fusion in the United States: analysis of trends from 1998 to 2008","volume":"37","author":"Rajaee","year":"2012","journal-title":"Spine"},{"key":"10.1016\/j.spinee.2025.01.031_bib0003","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1177\/21925682221103530","article-title":"Disparities in elective spine surgery for Medicaid beneficiaries: a systematic review","volume":"13","author":"Badin","year":"2023","journal-title":"Glob Spine J"},{"key":"10.1016\/j.spinee.2025.01.031_bib0004","doi-asserted-by":"crossref","first-page":"S173","DOI":"10.21037\/atm.2019.07.99","article-title":"Predictive modeling in spine surgery","volume":"7","author":"Malik","year":"2019","journal-title":"Ann Transl Med"},{"key":"10.1016\/j.spinee.2025.01.031_bib0005","doi-asserted-by":"crossref","first-page":"79S","DOI":"10.1177\/2192568220959037","article-title":"Prediction models in degenerative spine surgery: a systematic review","volume":"11","author":"Lubelski","year":"2021","journal-title":"Glob Spine J"},{"key":"10.1016\/j.spinee.2025.01.031_bib0006","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/s10916-017-0715-6","article-title":"A critical review for developing accurate and dynamic predictive models using machine learning methods in medicine and health care","volume":"41","author":"Alanazi","year":"2017","journal-title":"J Med Syst"},{"key":"10.1016\/j.spinee.2025.01.031_bib0007","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1007\/s41666-022-00114-1","article-title":"Explainable artificial intelligence for predictive modeling in healthcare","volume":"6","author":"Yang","year":"2022","journal-title":"J Healthc Inform Res"},{"key":"10.1016\/j.spinee.2025.01.031_bib0008","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2020.101822","article-title":"Automated machine learning: review of the state-of-the-art and opportunities for healthcare","volume":"104","author":"Waring","year":"2020","journal-title":"Artif Intell Med"},{"key":"10.1016\/j.spinee.2025.01.031_bib0009","series-title":"2019 IEEE 31st Int. Conf. Tools Artif. Intell. ICTAI","first-page":"1471","article-title":"Towards automated machine learning: evaluation and comparison of AutoML approaches and tools","author":"Truong","year":"2019"},{"key":"10.1016\/j.spinee.2025.01.031_bib0010","series-title":"Genetic Programming Theory and Practice XX","first-page":"1","article-title":"TPOT2: a new graph-based implementation of the tree-based pipeline optimization tool for automated machine learning editors","author":"Ribeiro","year":"2024"},{"key":"10.1016\/j.spinee.2025.01.031_bib0011","series-title":"Foundations of genetic programming","author":"Langdon","year":"2013"},{"key":"10.1016\/j.spinee.2025.01.031_bib0012","doi-asserted-by":"crossref","unstructured":"Kundrapu S, Noguez J. Chapter six - laboratory assessment of anemia. editor. Makowski GS. In: Advances in Clinical Chemistry, vol. 83, Elsevier; 2018. p. 197\u2013225. https:\/\/doi.org\/10.1016\/bs.acc.2017.10.006.","DOI":"10.1016\/bs.acc.2017.10.006"},{"key":"10.1016\/j.spinee.2025.01.031_bib0013","unstructured":"Haemoglobin concentrations for the diagnosis of anaemia and assessment of severity n.d. https:\/\/www.who.int\/publications-detail-redirect\/WHO-NMH-NHD-MNM-11.1 (Accessed March 7, 2024)."},{"key":"10.1016\/j.spinee.2025.01.031_bib0014","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1001\/jamasurg.2014.629","article-title":"Extended length of stay after surgery: complications, inefficient practice, or sick patients?","volume":"149","author":"Krell","year":"2014","journal-title":"JAMA Surg"},{"key":"10.1016\/j.spinee.2025.01.031_bib0015","first-page":"1","article-title":"mice: multivariate imputation by chained equations in R","volume":"45","author":"van Buuren","year":"2011","journal-title":"J Stat Softw"},{"key":"10.1016\/j.spinee.2025.01.031_bib0016","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/s40537-020-00305-w","article-title":"Survey on categorical data for neural networks","volume":"7","author":"Hancock","year":"2020","journal-title":"J Big Data"},{"key":"10.1016\/j.spinee.2025.01.031_bib0017","unstructured":"Poslavskaya E., Korolev A. Encoding categorical data: is there yet anything \u201chotter\u201d than one-hot encoding? 2023. https:\/\/doi.org\/10.48550\/arXiv.2312.16930."},{"key":"10.1016\/j.spinee.2025.01.031_bib0018","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1016\/j.beth.2020.05.002","article-title":"Supervised machine learning: a brief primer","volume":"51","author":"Jiang","year":"2020","journal-title":"Behav Ther"},{"key":"10.1016\/j.spinee.2025.01.031_bib0019","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.2025.01.031_bib0020","series-title":"Applications of Evolutionary Computing","first-page":"123","article-title":"Automating biomedical data science through tree-based pipeline optimization","author":"Olson","year":"2016"},{"key":"10.1016\/j.spinee.2025.01.031_bib0021","series-title":"Game theory","first-page":"210","article-title":"Shapley value","author":"Hart","year":"1989"},{"key":"10.1016\/j.spinee.2025.01.031_bib0022","series-title":"Adv Neural Inf Process Syst","first-page":"1","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"Lundberg","year":"2017"},{"key":"10.1016\/j.spinee.2025.01.031_bib0023","doi-asserted-by":"crossref","first-page":"2431","DOI":"10.1007\/s10916-011-9710-5","article-title":"Data mining in healthcare and biomedicine: a survey of the literature","volume":"36","author":"Yoo","year":"2012","journal-title":"J Med Syst"},{"key":"10.1016\/j.spinee.2025.01.031_bib0024","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1111\/j.1532-5415.1992.tb02017.x","article-title":"Anemia and hemoglobin levels in older persons: relationship with age, gender, and health status","volume":"40","author":"Salive","year":"1992","journal-title":"J Am Geriatr Soc"},{"key":"10.1016\/j.spinee.2025.01.031_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.archger.2024.105645","article-title":"The association between anemia, hospitalization, and all-cause mortality in patients with heart failure managed in primary care: an analysis of the Swedish heart failure registry","volume":"129","author":"Ugarph-Morawski","year":"2025","journal-title":"Arch Gerontol Geriatr"},{"key":"10.1016\/j.spinee.2025.01.031_bib0026","doi-asserted-by":"crossref","DOI":"10.1001\/jamasurg.2022.0172","article-title":"Comparison of electronic frailty metrics for prediction of adverse outcomes of abdominal surgery","volume":"157","author":"Le","year":"2022","journal-title":"JAMA Surg"},{"key":"10.1016\/j.spinee.2025.01.031_bib0027","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00586-020-06651-w","article-title":"Validation of a surgical invasiveness index in patients with lumbar spinal disorders registered in the Spine Tango registry","volume":"30","author":"Holzer","year":"2021","journal-title":"Eur Spine J Off Publ Eur Spine Soc Eur Spinal Deform Soc Eur Sect Cerv Spine Res Soc"},{"key":"10.1016\/j.spinee.2025.01.031_bib0028","doi-asserted-by":"crossref","unstructured":"Steyerberg EW. In: Steyerberg EW, editor. Applications of prediction models editor In: Clin. Predict. Models pract. Approach dev. New York, NY: 2009. p. 11\u201331. https:\/\/doi.org\/10.1007\/978-0-387-77244-8_2.","DOI":"10.1007\/978-0-387-77244-8_2"},{"key":"10.1016\/j.spinee.2025.01.031_bib0029","doi-asserted-by":"crossref","first-page":"364","DOI":"10.3171\/2020.6.SPINE201024","article-title":"Predicting nonroutine discharge in patients undergoing surgery for vertebral column tumors","volume":"34","author":"Ehresman","year":"2020","journal-title":"J Neurosurg Spine"},{"key":"10.1016\/j.spinee.2025.01.031_bib0030","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.2025.01.031_bib0031","doi-asserted-by":"crossref","DOI":"10.1016\/j.jns.2020.117184","article-title":"Systematic review and evaluation of predictive modeling algorithms in spinal surgeries","volume":"420","author":"Romiyo","year":"2021","journal-title":"J Neurol Sci"},{"key":"10.1016\/j.spinee.2025.01.031_bib0032","doi-asserted-by":"crossref","first-page":"736","DOI":"10.3171\/2016.10.SPINE16197","article-title":"Development of a preoperative predictive model for major complications following adult spinal deformity surgery","volume":"26","author":"Scheer","year":"2017","journal-title":"J Neurosurg Spine"},{"key":"10.1016\/j.spinee.2025.01.031_bib0033","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.spinee.2013.10.043","article-title":"Predicting medical complications after spine surgery: a validated model using a prospective surgical registry","volume":"14","author":"Lee","year":"2014","journal-title":"Spine J Off J North Am Spine Soc"},{"key":"10.1016\/j.spinee.2025.01.031_bib0034","first-page":"792","article-title":"Efficient Shapley explanation for features importance estimation under uncertainty","volume":"12261","author":"Li","year":"2020","journal-title":"Med Image Comput Comput-Assist Interv MICCAI Int Conf Med Image Comput Comput-Assist Interv"},{"key":"10.1016\/j.spinee.2025.01.031_bib0035","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1097\/BRS.0000000000003910","article-title":"Application of cooperative game theory principles to interpret machine learning models of nonhome discharge following spine surgery","volume":"46","author":"Martini","year":"2021","journal-title":"Spine"},{"key":"10.1016\/j.spinee.2025.01.031_bib0036","doi-asserted-by":"crossref","first-page":"E490","DOI":"10.1093\/neuros\/nyaa050","article-title":"Preoperative opioid use and clinical outcomes in spine surgery: a systematic review","volume":"86","author":"Yerneni","year":"2020","journal-title":"Neurosurgery"},{"key":"10.1016\/j.spinee.2025.01.031_bib0037","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1177\/2192568220979821","article-title":"Spine surgery and preoperative hemoglobin, hematocrit, and hemoglobin A1c: a systematic review","volume":"12","author":"Suresh","year":"2022","journal-title":"Glob Spine J"},{"key":"10.1016\/j.spinee.2025.01.031_bib0038","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1177\/2192568217699404","article-title":"Effect of preoperative anemia on the outcomes of anterior cervical discectomy and fusion","volume":"7","author":"Phan","year":"2017","journal-title":"Glob Spine J"},{"key":"10.1016\/j.spinee.2025.01.031_bib0039","first-page":"267","article-title":"The effect of anaemia on hospital length of stay in lumbar decompression and fusion procedures","volume":"25","author":"Sanoufa","year":"2015","journal-title":"J Perioper Pract"},{"key":"10.1016\/j.spinee.2025.01.031_bib0040","doi-asserted-by":"crossref","first-page":"e398","DOI":"10.1016\/j.wneu.2020.01.023","article-title":"Utility of preoperative laboratory testing in assessing risk of adverse outcomes after anterior cervical discectomy and fusion: insights from National Surgical Registry","volume":"136","author":"Zreik","year":"2020","journal-title":"World Neurosurg"},{"issue":"Suppl 8","key":"10.1016\/j.spinee.2025.01.031_bib0041","first-page":"S106","article-title":"Outpatient minimally invasive lumbar interbody: fusion predictive factors and clinical results","volume":"41","author":"Smith","year":"2016","journal-title":"Spine"},{"key":"10.1016\/j.spinee.2025.01.031_bib0042","doi-asserted-by":"crossref","DOI":"10.1016\/j.clineuro.2021.107017","article-title":"Effects of preoperative nutritional status on complications and readmissions after posterior lumbar decompression and fusion for spondylolisthesis: a propensity-score analysis","volume":"211","author":"Elsamadicy","year":"2021","journal-title":"Clin Neurol Neurosurg"},{"key":"10.1016\/j.spinee.2025.01.031_bib0043","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1016\/j.spinee.2021.11.015","article-title":"The performance of frailty in predictive modeling of short-term outcomes in the surgical management of metastatic tumors to the spine","volume":"22","author":"Bakhsheshian","year":"2022","journal-title":"Spine J Off J North Am Spine Soc"},{"key":"10.1016\/j.spinee.2025.01.031_bib0044","doi-asserted-by":"crossref","first-page":"e353","DOI":"10.1016\/j.wneu.2020.08.138","article-title":"A comparison of the Elixhauser and Charlson comorbidity indices: predicting In-hospital complications following anterior lumbar interbody fusions","volume":"144","author":"Baron","year":"2020","journal-title":"World Neurosurg"}],"container-title":["The Spine Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S152994302500052X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S152994302500052X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T20:58:36Z","timestamp":1762549116000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S152994302500052X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8]]},"references-count":44,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["S152994302500052X"],"URL":"https:\/\/doi.org\/10.1016\/j.spinee.2025.01.031","relation":{},"ISSN":["1529-9430"],"issn-type":[{"value":"1529-9430","type":"print"}],"subject":[],"published":{"date-parts":[[2025,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Preoperative anemia is an unsuspecting driver of machine learning prediction of adverse outcomes after lumbar spinal fusion","name":"articletitle","label":"Article Title"},{"value":"The Spine Journal","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.spinee.2025.01.031","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"}]}}