[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119533-en":3,"doc-seo-119533-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119533,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Machine Learning Did Not Outperform Conventional Competing Risk Modeling to Predict Revision Arthroplasty","Estimating revision risk after arthroplasty supports shared decision-making for both patients and surgeons, yet available prediction tools often underperform. Differences in performance may stem from reliance on conventional survival or competing-risk approaches such as traditional survivorship estimators (e.g., Kaplan-Meier) and competing risk estimators. Advances in machine learning survival analysis offer potential improvements for clinical decision support. This study evaluates whether machine learning methods outperform conventional modeling for predicting revision after arthroplasty.","University of Groningen  \nMachine Learning Did Not Outperform Conventional Competing Risk Modeling to Predict Revision Arthroplasty  \nMachine Learning Consortium; Oosterhoff, Jacobien H. F. ; de Hond, Anne A. H. ; Peters, Rinne M. ; van Steenbergen, Liza N. ; Sorel, Juliette C. ; Zijlstra, Wierd P. ; Poolman, Rudolf W. ; Ring, David; Jutte, Paul C.  \nPublished in:  \nClinical Orthopaedics and Related Research  \nDOI:  \n10.1097/CORR.0000000000003018  \nIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nPublication date: 2024  \nLink to publication in University of Groningen/UMCG research database  \nCitation for published version (APA):  \nMachine Learning Consortium, Oosterhoff, J. H. F. , de Hond, A. A. H. , Peters, R. M. , van Steenbergen, L. N. , Sorel, J. C. , Zijlstra, W. P. , Poolman, R. W. , Ring, D. , Jutte, P. C. , Kerkhoffs, G. M. M. J. , Putter, H. , Steyerberg, E. W. , & Doornberg, J. N. (2024) . Machine Learning Did Not Outperform Conventional Competing Risk Modeling to Predict Revision Arthroplasty. Clinical Orthopaedics and Related Research, 482(8), 1472-1482 . [https://doi.org/10.1097/CORR.0000000000003018](https://doi.org/10.1097/CORR.0000000000003018)  \nCopyright  \nOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons) .  \nThe publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment.  \nTake-down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.)  \nDownload date: 24-12-2025  \nDownloaded from [http://journals.lww.com/clinorthop by BhDMf5ePHKav1zEoum1tQfN4a](http://journals.lww.com/clinorthop by BhDMf5ePHKav1zEoum1tQfN4a)+kJLhEZgbsIHo4XMi0hCy wCX 1AWnYQp/ I l Qr HD3i 3 D0OdRyi 7TvS Fl4Cf3VC1y0abggQZXdtwnfKZBYtws= on 10/31/2024  \nClin Orthop Relat Res (2024) 482:1472-1482 DOI 10.1097/CORR.0000000000003018  \nClinical Research  \nMachine Learning Did Not Outperform Conventional Competing Risk Modeling to Predict Revision Arthroplasty  \nJacobien H. F. OosterhoffMD, PhD1,2, Anne A. H. de Hond PhD3,4,5, Rinne M. Peters MD, PhD6,  \nLiza N. van Steenbergen PhD7, Juliette C. Sorel MD8, Wierd P. Zijlstra MD, PhD6, Rudolf W. Poolman MD, PhD8, David Ring MD, PhD9, Paul C. Jutte MD, PhD10, Gino M. M. J. Kerkhoffs MD, PhD1, Hein Putter PhD4, Ewout W. Steyerberg MSc, PhD3,4, Job N. Doornberg MD, PhD10, and the Machine Learning Consortium*  \nReceived: 11 July 2023 / Accepted: 1 February 2024 / Published online: 12 March 2024 Copyright © 2024 by the Association of Bone and Joint Surgeons  \nAbstract  \nBackground Estimating the risk of revision after arthroplasty could inform patient and surgeon decision-making. However, there is a lack of well-performing prediction models assisting in this task, which may be due to current conventional modeling approaches such as traditional  \nsurvivorship estimators (such as Kaplan-Meier) or competing risk estimator","cbCaimwSWEG1DUh1","https://ap.wps.com/l/cbCaimwSWEG1DUh1","pdf",921123,1,12,"English","en",105,"# Abstract\n## Background and objective\n## Methods (comparative assessment)","[{\"question\":\"What is the clinical problem addressed in the study?\",\"answer\":\"The study targets estimating the risk of revision after arthroplasty to support decision-making for patients and surgeons.\"},{\"question\":\"Why might current prediction models be insufficient?\",\"answer\":\"Conventional modeling approaches may lack performance, including traditional survivorship estimators such as Kaplan-Meier and competing risk estimators.\"},{\"question\":\"What comparison does the research make?\",\"answer\":\"It compares machine learning survival analysis approaches against conventional competing-risk modeling for predicting revision after arthroplasty.\"}]","Machine Learning Did Not Outperform Conventional Competing Risk Modeling to Predict Revision Arthroplasty | 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