[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121650-en":3,"doc-seo-121650-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},121650,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Systematic review finds “Spin” practices and poor reporting standards in studies on machine learning-based prediction models","Objectives: Evaluate the presence and frequency of spin practices and poor reporting standards in studies developing and/or validating clinical prediction models using supervised machine learning. Methods: A systematic PubMed search (01/2018–12/2019) identified diagnostic and prognostic prediction model studies without restrictions on data source, outcome, or clinical specialty. Results: 152 studies were included; when discrimination was reported, precision estimates were often missing, and many recommendations lacked external validation. Conclusion: Spin and inadequate reporting also occur in machine learning prediction-model studies, and a tailored identification framework is needed.","University of Birmingham  \nSystematic review finds “Spin” practices and poor reporting standards in studies on machine learningbased prediction models  \nNavarro, Constanza L. Andaur; Damen, Johanna AA. ; Takada, Toshihiko; Nijman, Steven W.J. ; Dhiman, Paula; Ma, Jie; Collins, Gary S. ; Bajpai, Ram; Riley, Richard D. ; Moons, Karel  \nGM. ; Hooft, Lotty DOI:  \n10.1016/j.jclinepi.2023.03.024  \nLicense:  \nCreative Commons: Attribution (CC BY)  \nDocument Version  \nPublisher's PDF, also known as Version of record Citation for published version (Harvard):  \nNavarro, CLA, Damen, JAA, Takada, T, Nijman, SWJ, Dhiman, P, Ma, J, Collins, GS, Bajpai, R, Riley, RD, Moons, KGM & Hooft, L 2023, 'Systematic review finds “Spin” practices and poor reporting standards in studies on machine learning-based prediction models', Journal of Clinical Epidemiology, vol. 158, pp. 99-110. [https://doi.org/10.1016/j.jclinepi.2023.03.024](https://doi.org/10.1016/j.jclinepi.2023.03.024)  \nLink to publication on Research at Birmingham portal  \nGeneral rights  \nUnless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. The express permission of the copyright holder must be obtained for any use of this material other than for purposes permitted by law.  \n•Users may freely distribute the URL that is used to identify this publication.  \n•Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research.  \n•User may use extracts from the document in line with the concept of ‘fair dealing’ under the Copyright, Designs and Patents Act 1988 (?)  \n•Users may not further distribute the material nor use it for the purposes of commercial gain.  \nWhere a licence is displayed above, please note the terms and conditions of the licence govern your use of this document.  \nWhen citing, please reference the published version.  \nTake down policy  \nWhile the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.  \nIf you believe that this is the case for this document, [please contact UBIRA@lists.bham.ac.uk](please contact UBIRA@lists.bham.ac.uk) providing details and we will remove access to the work immediately and investigate.  \nDownload date: 03. Aug. 2026  \nJournal of Clinical Epidemiology 158 (2023) 99e110  \nREVIEW  \nSystematic review finds ‘‘spin’’ practices and poor reporting standards in studies on machine learning-based prediction models  \nConstanza L. Andaur Navarroa,b, *, Johanna A.A. Damena,b, Toshihiko Takadaa, Steven W.J. Nijmana, Paula Dhimanc,d, Jie Mac, Gary S. Collinsc,d, Ram Bajpaie, Richard D. Rileye, Karel G.M. Moonsa,b, Lotty Hoofta,b  \naJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands bCochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands  \ncCenter for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK  \ndNIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK eCentre for Prognosis Research, School of Medicine, Keele University, Keele, UK  \nAccepted 28 March 2023; Published online 5 April 2023  \nAbstract  \nObjectives: We evaluated the presence and frequency of spin practices and poor reporting standards in studies that developed and/or validated clinical prediction models using supervised machine learning techniques.  \nStudy Design and Setting: We systematically searched PubMed from 01/2018 to 12/2019 to identify diagnostic and prognostic prediction model studies using supervised machine learning. No restrictions were placed on data source, outcome, or clinical specialty.  \nResults: We included 152 studies: 38% reporte","cbCaipY4LYejhnq7","https://ap.wps.com/l/cbCaipY4LYejhnq7","pdf",383717,1,13,"English","en",105,"# Abstract\n## Objectives\n## Study design and setting\n## Results\n## Conclusion\n# Keywords\n# Systematic review registration","[{\"question\":\"What did the systematic review evaluate?\",\"answer\":\"It assessed the presence and frequency of spin practices and poor reporting standards in supervised machine learning–based clinical prediction model studies.\"},{\"question\":\"How were studies selected?\",\"answer\":\"The review systematically searched PubMed from 01/2018 to 12/2019 to identify diagnostic and prognostic prediction model studies using supervised machine learning.\"},{\"question\":\"What were the key findings about model reporting and validation?\",\"answer\":\"Discrimination was commonly described without precision estimates, and many abstracts and main texts recommended clinical use without external validation.\"}]","Systematic review finds “Spin” practices and poor reporting standards in studies on machine learning-based prediction models | 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