{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T05:22:30Z","timestamp":1793510550953,"version":"4.5.0"},"update-to":[{"DOI":"10.1002\/14651858.cd013606","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2023,9,8]],"date-time":"2023-09-08T00:00:00Z","timestamp":1694131200000}}],"reference-count":304,"publisher":"Wiley","issue":"9","content-domain":{"domain":["wiley.com","cochranelibrary.com"],"crossmark-restriction":true},"short-container-title":[],"DOI":"10.1002\/14651858.cd013606.pub2","type":"journal-article","created":{"date-parts":[[2023,9,8]],"date-time":"2023-09-08T07:21:42Z","timestamp":1694157702000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis"],"prefix":"10.1002","volume":"2023","author":[{"given":"Kelly","family":"Reeve","sequence":"additional","affiliation":[{"name":"Epidemiology, Biostatistics and Prevention Institute; University of Z\u00fcrich; Zurich Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Begum Irmak","family":"On","sequence":"additional","affiliation":[{"name":"Institute for Medical Information Processing, Biometry and Epidemiology; Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen; Munich Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joachim","family":"Havla","sequence":"additional","affiliation":[{"name":"lnstitute of Clinical Neuroimmunology; Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen; Munich Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacob","family":"Burns","sequence":"additional","affiliation":[{"name":"Institute for Medical Information Processing, Biometry and Epidemiology; Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen; Munich Germany"},{"name":"Pettenkofer School of Public Health; Munich Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martina A","family":"Gosteli-Peter","sequence":"additional","affiliation":[{"name":"University Library; University of Zurich; Zurich Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Albraa","family":"Alabsawi","sequence":"additional","affiliation":[{"name":"Institute for Medical Information Processing, Biometry and Epidemiology; Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen; Munich Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zoheir","family":"Alayash","sequence":"additional","affiliation":[{"name":"Institute for Medical Information Processing, Biometry and Epidemiology; Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen; Munich Germany"},{"name":"Institute of Health Services Research in Dentistry; University of M\u00fcnster; Muenster Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"G\u00f6tschi","sequence":"additional","affiliation":[{"name":"Epidemiology, Biostatistics and Prevention Institute; University of Z\u00fcrich; Zurich Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heidi","family":"Seibold","sequence":"additional","affiliation":[{"name":"IGDORE; Munich Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ulrich","family":"Mansmann","sequence":"additional","affiliation":[{"name":"Institute for Medical Information Processing, Biometry and Epidemiology; Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen; Munich Germany"},{"name":"Pettenkofer School of Public Health; Munich Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ulrike","family":"Held","sequence":"additional","affiliation":[{"name":"Epidemiology, Biostatistics and Prevention Institute; University of Z\u00fcrich; Zurich Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,9,8]]},"reference":[{"key":"10.1002\/14651858.CD013606.pub2-BIB0001|cit1","doi-asserted-by":"crossref","first-page":"102658","DOI":"10.1016\/j.msard.2020.102658","article-title":"A novel decision tree approach to predict the probability of conversion to multiple sclerosis in Iranian patients with optic neuritis","volume":"47","author":"Abri Aghdam","year":"2021","journal-title":"Multiple Sclerosis and Related Disorders"},{"issue":"Pt 10","key":"10.1002\/14651858.CD013606.pub2-BIB0002|cit2","doi-asserted-by":"publisher","first-page":"2620","DOI":"10.1093\/brain\/awl208","article-title":"Magnetization transfer MRI metrics predict the accumulation of disability 8 years later in patients with multiple sclerosis","volume":"129","author":"Agosta","year":"2006","journal-title":"Brain"},{"issue":"4","key":"10.1002\/14651858.CD013606.pub2-BIB0003|cit3","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1002\/acn3.51324","article-title":"Leveraging electronic health records data to predict multiple sclerosis disease activity","volume":"8","author":"Ahuja","year":"2021","journal-title":"Annals of Clinical and Translational Neurology"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0004|cit4","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1186\/1471-2377-11-67","article-title":"Computational classifiers for predicting the short-term course of multiple sclerosis","volume":"11","author":"Bejarano","year":"2011","journal-title":"BMC Neurology"},{"issue":"5","key":"10.1002\/14651858.CD013606.pub2-BIB0005|cit5","doi-asserted-by":"publisher","first-page":"1361","DOI":"10.1007\/s11682-018-9942-9","article-title":"MRI-based prediction of conversion from clinically isolated syndrome to clinically definite multiple sclerosis using SVM and lesion geometry","volume":"13","author":"Bendfeldt","year":"2019","journal-title":"Brain Imaging and Behavior"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0006|cit6","unstructured":"Bendfeldt K Taschler B Gaetano L Madoerin P Kuster P Mueller-Lenke N Predicting conversion to clinically definite multiple sclerosis using machine learning on the basis of cerebral grey matter segmentations 31st European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS); 2015 October 7-10; Barcelona (Spain) ECTRIMS"},{"issue":"Suppl 11","key":"10.1002\/14651858.CD013606.pub2-BIB0007|cit7","first-page":"498","article-title":"Predicting conversion to clinically definite multiple sclerosis using machine learning on the basis of cerebral grey matter segmentations","volume":"23","author":"Bendfeldt","year":"2015","journal-title":"Multiple Sclerosis Journal"},{"issue":"5","key":"10.1002\/14651858.CD013606.pub2-BIB0008|cit8","doi-asserted-by":"crossref","first-page":"1361","DOI":"10.1007\/s11682-018-9942-9","article-title":"MRI-based prediction of conversion from clinically isolated syndrome to clinically definite multiple sclerosis using SVM and lesion geometry","volume":"13","author":"Bendfeldt","year":"2018","journal-title":"Brain Imaging and Behavior"},{"issue":"1-2","key":"10.1002\/14651858.CD013606.pub2-BIB0009|cit9","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/S0022-510X(01)00572-X","article-title":"Predicting secondary progression in relapsing-remitting multiple sclerosis: a bayesian analysis","volume":"189","author":"Bergamaschi","year":"2001","journal-title":"Journal of the Neurological Sciences"},{"issue":"7","key":"10.1002\/14651858.CD013606.pub2-BIB0010|cit10","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1136\/jnnp.2006.107052","article-title":"Early prediction of the long term evolution of multiple sclerosis: the bayesian risk estimate for multiple sclerosis (BREMS) score","volume":"78","author":"Bergamaschi","year":"2007","journal-title":"Journal of Neurology, Neurosurgery and Psychiatry"},{"issue":"6","key":"10.1002\/14651858.CD013606.pub2-BIB0011|cit11","doi-asserted-by":"publisher","first-page":"981","DOI":"10.1111\/ene.12696","article-title":"BREMSO: a simple score to predict early the natural course of multiple sclerosis","volume":"22","author":"Bergamaschi","year":"2015","journal-title":"European Journal of Neurology"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0012|cit12","unstructured":"Bergamaschi R Montomoli C Mallucci G Bayesian risk estimate for multiple sclerosis at onset (BREMSO): a simple clinical score for the early prediction of multiple sclerosis long-term evolution 29th European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS); 2013 October 2-5; Copenhagen (Denmark) ECTRIMS"},{"issue":"Suppl 1","key":"10.1002\/14651858.CD013606.pub2-BIB0013|cit13","first-page":"338","article-title":"Bayesian risk estimate for multiple sclerosis at onset (BREMSO): a simple clinical score for the early prediction of multiple sclerosis long-term evolution","volume":"19","author":"Bergamaschi","year":"2013","journal-title":"Multiple Sclerosis Journal"},{"issue":"1","key":"10.1002\/14651858.CD013606.pub2-BIB0014|cit14","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1074\/mcp.M115.053256","article-title":"Protein-based classifier to predict conversion from clinically isolated syndrome to multiple sclerosis","volume":"15","author":"Borras","year":"2016","journal-title":"Molecular and Cellular Proteomics"},{"issue":"Suppl 11","key":"10.1002\/14651858.CD013606.pub2-BIB0015|cit15","first-page":"634","article-title":"Protein-based biomarker predicts conversion from clinically isolated syndrome to multiple sclerosis","volume":"21","author":"Comabella","year":"2015","journal-title":"Multiple Sclerosis Journal"},{"issue":"2","key":"10.1002\/14651858.CD013606.pub2-BIB0016|cit16","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1007\/s10072-019-04093-x","article-title":"The hidden information in patient-reported outcomes and clinician-assessed outcomes: multiple sclerosis as a proof of concept of a machine learning approach","volume":"41","author":"Brichetto","year":"2020","journal-title":"Journal of the Neurological Sciences"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0017|cit17","unstructured":"Tacchino A Fiorini S Ponzio M Barla A Verri A Battaglia MA Multiple sclerosis disease course prediction: a machine learning model based on patient reported and clinician assessed outcomes 7th Joint European Committee for Treatment and Research in Multiple Sclerosis-Americas Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS-ACTRIMS); 2017 October 25-28; Paris (France) ECTRIMS"},{"issue":"Suppl 3","key":"10.1002\/14651858.CD013606.pub2-BIB0018|cit18","first-page":"58","article-title":"Multiple sclerosis disease course prediction: a machine learning model based on patient reported and clinician assessed outcomes","volume":"23","author":"Tacchino","year":"2017","journal-title":"Multiple Sclerosis Journal"},{"issue":"Suppl 4","key":"10.1002\/14651858.CD013606.pub2-BIB0019|cit19","first-page":"121","article-title":"The grey matter basis of disability progression in multiple sclerosis","volume":"18","author":"Calabrese","year":"2012","journal-title":"Multiple Sclerosis Journal"},{"issue":"1","key":"10.1002\/14651858.CD013606.pub2-BIB0020|cit20","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1002\/ana.23882","article-title":"The changing clinical course of multiple sclerosis: a matter of gray matter","volume":"74","author":"Calabrese","year":"2013","journal-title":"Annals of Neurology"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0021|cit21","doi-asserted-by":"crossref","first-page":"106180","DOI":"10.1016\/j.cmpb.2021.106180","article-title":"Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression","volume":"208","author":"De Brouwer","year":"2021","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0022|cit22","unstructured":"De Brouwer E Peeters L Becker T Altintas A Soysal A Van Wijmeersch B Introducing machine learning for full MS patient trajectories improves predictions for disability score progression 35th European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS); 2019 September 11-13; Stockholm (Sweden) ECTRIMS"},{"issue":"Suppl 2","key":"10.1002\/14651858.CD013606.pub2-BIB0023|cit23","first-page":"63","article-title":"Introducing machine learning for full MS patient trajectories improves predictions for disability score progression","volume":"25","author":"De Brouwer","year":"2019","journal-title":"Multiple Sclerosis Journal"},{"issue":"9","key":"10.1002\/14651858.CD013606.pub2-BIB0024|cit24","doi-asserted-by":"publisher","first-page":"1478","DOI":"10.1016\/j.apmr.2009.03.018","article-title":"Physical and cognitive functioning after 3 years can be predicted using information from the diagnostic process in recently diagnosed multiple sclerosis","volume":"90","author":"Groot","year":"2009","journal-title":"Archives of Physical Medicine and Rehabilitation"},{"issue":"2","key":"10.1002\/14651858.CD013606.pub2-BIB0025|cit25","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1177\/1352458510385506","article-title":"Prognostic value of cerebrospinal fluid analysis at the time of a first demyelinating event","volume":"17","author":"Gout","year":"2011","journal-title":"Multiple Sclerosis Journal"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0026|cit26","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1186\/1755-8794-2-46","article-title":"Prediction of acute multiple sclerosis relapses by transcription levels of peripheral blood cells","volume":"2","author":"Gurevich","year":"2009","journal-title":"BMC Medical Genomics"},{"issue":"Suppl 15","key":"10.1002\/14651858.CD013606.pub2-BIB0027|cit27","first-page":"P3.2","article-title":"Constructing a molecular model of disease severity in multiple sclerosis","volume":"92","author":"Barbour","year":"2019","journal-title":"Neurology"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0028|cit28","doi-asserted-by":"crossref","unstructured":"Barbour C Kosa P Varosanec M Greenwood M Bielekova B Molecular models of multiple sclerosis severity identify heterogeneity of pathogenic mechanisms medRxiv https:\/\/doi.org\/10.1101\/2020.05.18.20105932","DOI":"10.1101\/2020.05.18.20105932"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0029|cit29","first-page":"23","article-title":"Constructing a molecular model of disease severity in multiple sclerosis","volume":"25","author":"Barbour","year":"2019","journal-title":"Multiple Sclerosis Journal"},{"issue":"1","key":"10.1002\/14651858.CD013606.pub2-BIB0030|cit30","doi-asserted-by":"publisher","first-page":"7670","DOI":"10.1038\/s41467-022-35357-4","article-title":"Molecular models of multiple sclerosis severity identify heterogeneity of pathogenic mechanisms","volume":"13","author":"Kosa","year":"2022","journal-title":"Nature Communications"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0031|cit31","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1016\/j.nicl.2018.05.003","article-title":"Baseline biomarkers of connectome disruption and atrophy predict future processing speed in early multiple sclerosis","volume":"19","author":"Kuceyeski","year":"2018","journal-title":"NeuroImage: Clinical"},{"issue":"4","key":"10.1002\/14651858.CD013606.pub2-BIB0032|cit32","doi-asserted-by":"publisher","first-page":"205521731988598","DOI":"10.1177\/2055217319885983","article-title":"Machine learning in secondary progressive multiple sclerosis: an improved predictive model for short-term disability progression","volume":"5","author":"Law","year":"2019","journal-title":"Multiple Sclerosis Journal Experimental Translational and Clinical"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0033|cit33","unstructured":"Law MT Traboulsee AL Li DK Carruthers RL Freedman MS Kolind SH Machine learning outperforms linear regression for predicting disability progression in SPMS 34th European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS); 2018 October 10-12; Berlin (Germany) ECTRIMS"},{"issue":"Suppl 2","key":"10.1002\/14651858.CD013606.pub2-BIB0034|cit34","first-page":"1025","article-title":"Machine learning outperforms linear regression for predicting disability progression in SPMS","volume":"24","author":"Law","year":"2018","journal-title":"Multiple Sclerosis Journal"},{"issue":"Suppl 2","key":"10.1002\/14651858.CD013606.pub2-BIB0035|cit35","first-page":"198","article-title":"SMILE: a predictive model for scoring the severity of relapses in multIple sclerosis","volume":"25","author":"Lejeune","year":"2019","journal-title":"Multiple Sclerosis Journal"},{"issue":"2","key":"10.1002\/14651858.CD013606.pub2-BIB0036|cit36","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1007\/s00415-020-10154-5","article-title":"SMILE: a predictive model for scoring the severity of relapses in multIple sclerosis","volume":"268","author":"Lejeune","year":"2021","journal-title":"Journal of Neurology"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0037|cit37","unstructured":"Lejeune F Chatton A Laplaud DA Wiertlewski S Edan G Lepage E SCOPOUSEP: a predictive model for scoring the severity of relapses in multiple sclerosis 34th European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS); 2018 October 10-12; Berlin (Germany) ECTRIMS"},{"issue":"Suppl 2","key":"10.1002\/14651858.CD013606.pub2-BIB0038|cit38","first-page":"791","article-title":"SCOPOUSEP: a predictive model for scoring the severity of relapses in multiple sclerosis","volume":"24","author":"Lejeune","year":"2018","journal-title":"Multiple Sclerosis Journal"},{"issue":"Suppl 2","key":"10.1002\/14651858.CD013606.pub2-BIB0039|cit39","first-page":"605","article-title":"Aggressive form of multiple sclerosis can be predicted early after disease onset","volume":"25","author":"Malpas","year":"2019","journal-title":"Multiple Sclerosis Journal"},{"issue":"5","key":"10.1002\/14651858.CD013606.pub2-BIB0040|cit40","doi-asserted-by":"publisher","first-page":"1400","DOI":"10.1093\/brain\/awaa081","article-title":"Early clinical markers of aggressive multiple sclerosis","volume":"143","author":"Malpas","year":"2020","journal-title":"Brain"},{"issue":"7","key":"10.1002\/14651858.CD013606.pub2-BIB0041|cit41","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.1007\/s00415-008-0827-5","article-title":"A multifactorial prognostic index in multiple sclerosis. Cerebrospinal fluid IgM oligoclonal bands and clinical features to predict the evolution of the disease","volume":"255","author":"Mandrioli","year":"2008","journal-title":"Journal of Neurology"},{"issue":"8","key":"10.1002\/14651858.CD013606.pub2-BIB0042|cit42","doi-asserted-by":"publisher","first-page":"1102","DOI":"10.1177\/1352458518783667","article-title":"Predicting risk of secondary progression in multiple sclerosis: a nomogram","volume":"25","author":"Manouchehrinia","year":"2019","journal-title":"Multiple Sclerosis Journal"},{"issue":"4","key":"10.1002\/14651858.CD013606.pub2-BIB0043|cit43","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1007\/s10072-011-0862-3","article-title":"Sensory evoked potentials to predict short-term progression of disability in multiple sclerosis","volume":"33","author":"Margaritella","year":"2012","journal-title":"Journal of the Neurological Sciences"},{"issue":"Suppl 11","key":"10.1002\/14651858.CD013606.pub2-BIB0044|cit44","first-page":"370","article-title":"Use of multiple biomarkers to improve the prediction of multiple sclerosis in patients with clinically isolated syndromes","volume":"23","author":"Martinelli","year":"2015","journal-title":"Journal of the Neurological Sciences"},{"issue":"5","key":"10.1002\/14651858.CD013606.pub2-BIB0045|cit45","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1111\/ane.12761","article-title":"Multiple biomarkers improve the prediction of multiple sclerosis in clinically isolated syndromes","volume":"136","author":"Martinelli","year":"2017","journal-title":"Acta Neurologica Scandinavica"},{"issue":"8","key":"10.1002\/14651858.CD013606.pub2-BIB0046|cit46","doi-asserted-by":"publisher","first-page":"2328","DOI":"10.1007\/s00415-020-09850-z","article-title":"Predicting onset of secondary-progressive multiple sclerosis using genetic and non-genetic factors","volume":"267","author":"Misicka","year":"2020","journal-title":"Journal of Neurology"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0047|cit47","doi-asserted-by":"crossref","first-page":"104416","DOI":"10.1016\/j.compbiomed.2021.104416","article-title":"Machine learning in diagnosis and disability prediction of multiple sclerosis using optical coherence tomography","volume":"133","author":"Montolio","year":"2021","journal-title":"Computers in Biology and Medicine"},{"issue":"1","key":"10.1002\/14651858.CD013606.pub2-BIB0048|cit48","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1186\/s12974-019-1440-5","article-title":"Cerebrospinal fluid biomarkers for predicting development of multiple sclerosis in acute optic neuritis: a population-based prospective cohort study","volume":"16","author":"Olesen","year":"2019","journal-title":"Journal of Neuroinflammation"},{"issue":"6","key":"10.1002\/14651858.CD013606.pub2-BIB0049|cit49","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.31925\/farmacia.2020.6.23","article-title":"The development and validation of a disability and outcome prediction algorithm in multiple sclerosis patients","volume":"68","author":"Oprea","year":"2020","journal-title":"Farmacia"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0050|cit50","unstructured":"Copetti M Fontana A Freudensprung U De Moor C Hyde R Bovis F Predicting MS disease progression remains a significant challenge: results from advanced statistical models of RCT placebo arms 7th Joint European Committee for Treatment and Research in Multiple Sclerosis-Americas Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS-ACTRIMS); 2017 October 25-28; Paris (France) ECTRIMS"},{"issue":"Suppl 3","key":"10.1002\/14651858.CD013606.pub2-BIB0051|cit51","first-page":"113","article-title":"Predicting MS disease progression remains a significant challenge: results from advanced statistical models of RCT placebo arms","volume":"23","author":"Copetti","year":"2017","journal-title":"Multiple Sclerosis Journal"},{"issue":"14","key":"10.1002\/14651858.CD013606.pub2-BIB0052|cit52","doi-asserted-by":"publisher","first-page":"1828","DOI":"10.1177\/1352458519887343","article-title":"Predicting disability progression in multiple sclerosis: insights from advanced statistical modeling","volume":"26","author":"Pellegrini","year":"2019","journal-title":"Multiple Sclerosis Journal"},{"issue":"1","key":"10.1002\/14651858.CD013606.pub2-BIB0053|cit53","doi-asserted-by":"publisher","first-page":"21038","DOI":"10.1038\/s41598-020-78212-6","article-title":"Prediction of disease progression and outcomes in multiple sclerosis with machine learning","volume":"10","author":"Pinto","year":"2020","journal-title":"Scientific Reports"},{"issue":"8","key":"10.1002\/14651858.CD013606.pub2-BIB0054|cit54","doi-asserted-by":"crossref","first-page":"2503","DOI":"10.1111\/ene.14859","article-title":"A novel prognostic score to assess the risk of progression in relapsing-remitting multiple sclerosis patients","volume":"28","author":"Pisani","year":"2021","journal-title":"European Journal of Neurology"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0055|cit55","unstructured":"Pisani AI Scalfari A Romualdi C Calabrese M The progressive multiple sclerosis score: a prognostic assistant tool in multiple sclerosis disease 35th European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS); 2019 September 11-13; Stockholm (Sweden) ECTRIMS"},{"issue":"Suppl 2","key":"10.1002\/14651858.CD013606.pub2-BIB0056|cit56","first-page":"62","article-title":"The progressive multiple sclerosis score: a prognostic assistant tool in multiple sclerosis disease","volume":"25","author":"Pisani","year":"2019","journal-title":"Multiple Sclerosis Journal"},{"issue":"12","key":"10.1002\/14651858.CD013606.pub2-BIB0057|cit57","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1016\/j.diii.2020.05.009","article-title":"Artificial intelligence to predict clinical disability in patients with multiple sclerosis using FLAIR MRI","volume":"101","author":"Roca","year":"2020","journal-title":"Diagnostic and Interventional Imaging"},{"issue":"Suppl 1","key":"10.1002\/14651858.CD013606.pub2-BIB0058|cit58","first-page":"43","article-title":"Earlier prognostication in primary progressive multiple sclerosis using MRI: a 15-year longitudinal study","volume":"24","author":"Filippi","year":"2017","journal-title":"European Journal of Neurology"},{"issue":"Suppl 3","key":"10.1002\/14651858.CD013606.pub2-BIB0059|cit59","first-page":"292","article-title":"Anticipation of long-term disability progression in PPMS using MRI: a 15-year longitudinal study","volume":"23","author":"Rocca","year":"2017","journal-title":"Multiple Sclerosis Journal"},{"issue":"11","key":"10.1002\/14651858.CD013606.pub2-BIB0060|cit60","doi-asserted-by":"publisher","first-page":"2814","DOI":"10.1093\/brain\/awx250","article-title":"Long-term disability progression in primary progressive multiple sclerosis: a 15-year study","volume":"140","author":"Rocca","year":"2017","journal-title":"Brain"},{"issue":"Pt 10","key":"10.1002\/14651858.CD013606.pub2-BIB0061|cit61","doi-asserted-by":"crossref","first-page":"2628","DOI":"10.1093\/brain\/awl222","article-title":"Grey matter damage predicts the evolution of primary progressive multiple sclerosis at 5 years","volume":"129","author":"Rovaris","year":"2006","journal-title":"Brain"},{"issue":"Suppl 1","key":"10.1002\/14651858.CD013606.pub2-BIB0062|cit62","first-page":"404","article-title":"A clinical prediction model for definite multiple sclerosis in patients with clinically isolated syndrome","volume":"20","author":"Runia","year":"2014","journal-title":"Multiple Sclerosis"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0063|cit63","volume-title":"Multiple Sclerosis - Predicting the Next Attack [Dissertation]","author":"Runia","year":"2015"},{"issue":"3","key":"10.1002\/14651858.CD013606.pub2-BIB0064|cit64","doi-asserted-by":"publisher","first-page":"e0230219","DOI":"10.1371\/journal.pone.0230219","article-title":"Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis","volume":"15","author":"Seccia","year":"2020","journal-title":"PLOS One"},{"issue":"Suppl 1","key":"10.1002\/14651858.CD013606.pub2-BIB0065|cit65","first-page":"P05.089","article-title":"Multiple sclerosis: a method to identify high risk for secondary progression","volume":"78","author":"Skoog","year":"2012","journal-title":"Neurology"},{"issue":"5","key":"10.1002\/14651858.CD013606.pub2-BIB0066|cit66","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1016\/j.msard.2014.04.004","article-title":"Continuous prediction of secondary progression in the individual course of multiple sclerosis","volume":"3","author":"Skoog","year":"2014","journal-title":"Multiple Sclerosis and Related Disorders"},{"issue":"Suppl 7","key":"10.1002\/14651858.CD013606.pub2-BIB0067|cit67","first-page":"P04.131","article-title":"A method to identify the risk of transition to the secondary progressive course in multiple sclerosis patients","volume":"80","author":"Tedeholm","year":"2013","journal-title":"Neurology"},{"issue":"Suppl 4","key":"10.1002\/14651858.CD013606.pub2-BIB0068|cit68","first-page":"91","article-title":"A new method to identify multiple sclerosis patients with a high risk for secondary progression","volume":"18","author":"Tedeholm","year":"2012","journal-title":"Multiple Sclerosis Journal"},{"issue":"3","key":"10.1002\/14651858.CD013606.pub2-BIB0069|cit69","doi-asserted-by":"publisher","first-page":"205521731987546","DOI":"10.1177\/2055217319875466","article-title":"Short-term prediction of secondary progression in a sliding window: a test of a predicting algorithm in a validation cohort","volume":"5","author":"Skoog","year":"2019","journal-title":"Multiple Sclerosis Journal - Experimental, Translational and Clinical"},{"issue":"6","key":"10.1002\/14651858.CD013606.pub2-BIB0070|cit70","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1177\/1352458510364633","article-title":"Analysis of multiple candidate genes in association with phenotypes of multiple sclerosis","volume":"16","author":"Sombekke","year":"2010","journal-title":"Multiple Sclerosis"},{"issue":"12","key":"10.1002\/14651858.CD013606.pub2-BIB0071|cit71","doi-asserted-by":"publisher","first-page":"1230","DOI":"10.1212\/01.wnl.0000276940.90309.15","article-title":"A composite score to predict short-term disease activity in patients with relapsing-remitting MS","volume":"69","author":"Sormani","year":"2007","journal-title":"Neurology"},{"issue":"10","key":"10.1002\/14651858.CD013606.pub2-BIB0072|cit72","doi-asserted-by":"publisher","first-page":"1346","DOI":"10.1177\/1352458516679893","article-title":"Quantifying risk of early relapse in patients with first demyelinating events: prediction in clinical practice","volume":"23","author":"Spelman","year":"2017","journal-title":"Multiple Sclerosis Journal"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0073|cit73","unstructured":"Szilasiov\u00e1 J Rosenberger J Mikula P Vitkov\u00e1 M Fedi\u010dov\u00e1 M Gdovinov\u00e1 Z Cognitive event-related potentials-the P300 wave is a prognostic factor of long-term disability progression in patients with multiple sclerosis Journal of Clinical Neurophysiology 10.1097\/WNP.0000000000000788"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0074|cit74","doi-asserted-by":"publisher","first-page":"2172","DOI":"10.12688\/f1000research.13114.2","article-title":"Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: a proof-of-principle study","volume":"6","author":"Tacchella","year":"2017","journal-title":"F1000Research"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0075|cit75","doi-asserted-by":"publisher","first-page":"2172","DOI":"10.12688\/f1000research.13114.1","article-title":"Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: a proof-of-principle study","volume":"6","author":"Tacchella","year":"2018","journal-title":"F1000Research"},{"issue":"12","key":"10.1002\/14651858.CD013606.pub2-BIB0076|cit76","doi-asserted-by":"crossref","first-page":"4834","DOI":"10.1007\/s00415-021-10605-7","article-title":"Machine learning classifier to identify clinical and radiological features relevant to disability progression in multiple sclerosis","volume":"268","author":"Tommasin","year":"2021","journal-title":"Journal of Neurology"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0077|cit77","first-page":"483","article-title":"Prediction of disease progression in multiple sclerosis patients using deep learning analysis of MRI data","volume":"102","author":"Tousignant","year":"2019","journal-title":"Proceedings of Machine Learning Research"},{"issue":"Suppl 3","key":"10.1002\/14651858.CD013606.pub2-BIB0078|cit78","first-page":"740","article-title":"Validation of a clinical risk score for long-term progression of MS","volume":"23","author":"Auren\u00e7\u00e3o","year":"2017","journal-title":"Multiple Sclerosis Journal"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0079|cit79","doi-asserted-by":"publisher","first-page":"105792","DOI":"10.1016\/j.clineuro.2020.105792","article-title":"Long-term MS secondary progression: derivation and validation of a clinical risk score","volume":"194","author":"Vasconcelos","year":"2020","journal-title":"Clinical Neurology and Neurosurgery"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0080|cit80","unstructured":"Vasconcelos CCF Thuler LCS Calvet Kallenbach Auren\u00e7\u00e3o JCK Papais-Alvarenga RM A proposal for a risk score for long-term progression of multiple sclerosis 31st European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS); 2015 October 7-10; Barcelona (Spain) ECTRIMS"},{"issue":"Suppl 11","key":"10.1002\/14651858.CD013606.pub2-BIB0081|cit81","first-page":"732","article-title":"A proposal for a risk score for long-term progression of multiple sclerosis","volume":"21","author":"Vasconcelos","year":"2015","journal-title":"Multiple Sclerosis Journal"},{"issue":"Pt 8","key":"10.1002\/14651858.CD013606.pub2-BIB0082|cit82","first-page":"1912","article-title":"Erratum: Pregnancy and multiple sclerosis (the PRIMS study) - clinical predictors of post-partum relapse","volume":"127","author":"Vukusic","year":"2004","journal-title":"Brain"},{"issue":"Pt 6","key":"10.1002\/14651858.CD013606.pub2-BIB0083|cit83","doi-asserted-by":"publisher","first-page":"1353","DOI":"10.1093\/brain\/awh152","article-title":"Pregnancy and multiple sclerosis (the PRIMS study) - clinical predictors of post-partum relapse","volume":"127","author":"Vukusic","year":"2004","journal-title":"Brain"},{"issue":"Pt 2","key":"10.1002\/14651858.CD013606.pub2-BIB0084|cit84","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1093\/brain\/114.2.1045","article-title":"The natural history of multiple sclerosis: a geographically based study. 3. Multivariate analysis of predictive factors and models of outcome","volume":"114","author":"Weinshenker","year":"1991","journal-title":"Brain"},{"issue":"4","key":"10.1002\/14651858.CD013606.pub2-BIB0085|cit85","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1001\/archneur.1996.00550040093018","article-title":"Long-term and short-term outcome of multiple sclerosis: a 3-year follow-up study","volume":"53","author":"Weinshenker","year":"1996","journal-title":"Archives of Neurology"},{"issue":"Suppl 4","key":"10.1002\/14651858.CD013606.pub2-BIB0086|cit86","first-page":"30","article-title":"Predicting clinical conversion to multiple sclerosis in patients with clinically isolated syndrome using machine learning techniques","volume":"18","author":"Ciccarelli","year":"2012","journal-title":"Multiple Sclerosis Journal"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0087|cit87","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1016\/j.nicl.2014.11.021","article-title":"Predicting outcome in clinically isolated syndrome using machine learning","volume":"7","author":"Wottschel","year":"2015","journal-title":"NeuroImage: Clinical"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0088|cit88","doi-asserted-by":"crossref","unstructured":"Wottschel V Ciccarelli O Chard DT Miller DH Alexander DC Prediction of second neurological attack in patients with clinically isolated syndrome using support vector machines 2013 International Workshop on Pattern Recognition in Neuroimaging 2013 82 5","DOI":"10.1109\/PRNI.2013.30"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0089|cit89","doi-asserted-by":"publisher","first-page":"102011","DOI":"10.1016\/j.nicl.2019.102011","article-title":"SVM recursive feature elimination analyses of structural brain MRI predicts near-term relapses in patients with clinically isolated syndromes suggestive of multiple sclerosis","volume":"24","author":"Wottschel","year":"2019","journal-title":"NeuroImage: Clinical"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0090|cit90","doi-asserted-by":"crossref","first-page":"579683","DOI":"10.3389\/fneur.2020.579683","article-title":"Development and validation of a five-gene signature to predict relapse-free survival in multiple sclerosis","volume":"11","author":"Ye","year":"2020","journal-title":"Frontiers in Neurology"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0091|cit91","volume-title":"Deep Learning and Data Labeling for Medical Applications","author":"Yoo","year":"2016"},{"issue":"3","key":"10.1002\/14651858.CD013606.pub2-BIB0092|cit92","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1080\/21681163.2017.1356750","article-title":"Deep learning of brain lesion patterns and user-defined clinical and MRI features for predicting conversion to multiple sclerosis from clinically isolated syndrome","volume":"7","author":"Yoo","year":"2019","journal-title":"Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization"},{"issue":"1","key":"10.1002\/14651858.CD013606.pub2-BIB0093|cit93","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1186\/s12883-020-01672-w","article-title":"Machine learning analysis of motor evoked potential time series to predict disability progression in multiple sclerosis","volume":"20","author":"Yperman","year":"2020","journal-title":"BMC Neurology"},{"issue":"Suppl 2","key":"10.1002\/14651858.CD013606.pub2-BIB0094|cit94","first-page":"874","article-title":"Machine learning analysis of motor evoked potential time series to predict disability progression in multiple sclerosis","volume":"25","author":"Yperman","year":"2019","journal-title":"Multiple Sclerosis Journal"},{"issue":"2 Pt 2","key":"10.1002\/14651858.CD013606.pub2-BIB0095|cit95","first-page":"28","article-title":"Assessment of the risk of the transition of a monofocal clinically isolated syndrome to clinically definite multiple sclerosis","volume":"113","author":"Zakharov","year":"2013","journal-title":"Zhurnal Nevrologii i Psikhiatrii Imeni S.S. Korsakova"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0096|cit96","doi-asserted-by":"publisher","first-page":"101593","DOI":"10.1016\/j.nicl.2018.11.003","article-title":"Predicting conversion from clinically isolated syndrome to multiple sclerosis - an imaging-based machine learning approach","volume":"21","author":"Zhang","year":"2019","journal-title":"NeuroImage: Clinical"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0097|cit97","unstructured":"Chitnis T Zhao Y Healy BC Rotstein D Guttmann CRG Bakshi R Predicting clinical course in multiple sclerosis using machine learning 6th Joint European Committee for Treatment and Research in Multiple Sclerosis-Americas Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS-ACTRIMS); 2014 September 10-13; Boston (MA) ECTRIMS"},{"issue":"Suppl 1","key":"10.1002\/14651858.CD013606.pub2-BIB0098|cit98","first-page":"404","article-title":"Predicting clinical course in multiple sclerosis using machine learning","volume":"20","author":"Chitnis","year":"2014","journal-title":"Multiple Sclerosis Journal"},{"issue":"Suppl 1","key":"10.1002\/14651858.CD013606.pub2-BIB0099|cit99","first-page":"160","article-title":"Ensemble learning for predicting multiple sclerosis disease course","volume":"25","author":"Zhao","year":"2019","journal-title":"Multiple Sclerosis Journal"},{"issue":"4","key":"10.1002\/14651858.CD013606.pub2-BIB0100|cit100","doi-asserted-by":"publisher","first-page":"e0174866","DOI":"10.1371\/journal.pone.0174866","article-title":"Exploration of machine learning techniques in predicting multiple sclerosis disease course","volume":"12","author":"Zhao","year":"2017","journal-title":"PLOS One"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0101|cit101","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1038\/s41746-020-00361-9","article-title":"Ensemble learning predicts multiple sclerosis disease course in the SUMMIT study","volume":"3","author":"Zhao","year":"2020","journal-title":"NPJ Digital Medicine"},{"issue":"6","key":"10.1002\/14651858.CD013606.pub2-BIB0102|cit102","first-page":"vi31","article-title":"Measuring disability progression in multiple sclerosis","volume":"253","author":"Achiron","year":"2006","journal-title":"Journal of Neurology"},{"issue":"9","key":"10.1002\/14651858.CD013606.pub2-BIB0103|cit103","doi-asserted-by":"publisher","first-page":"1202","DOI":"10.1177\/1352458515613641","article-title":"Deregulation of microRNA-181c in cerebrospinal fluid of patients with clinically isolated syndrome is associated with early conversion to relapsing-remitting multiple sclerosis","volume":"22","author":"Ahlbrecht","year":"2016","journal-title":"Multiple Sclerosis Journal"},{"issue":"Suppl 1","key":"10.1002\/14651858.CD013606.pub2-BIB0104|cit104","first-page":"25","article-title":"Fifty years untreated prognosis of multiple sclerosis based on an incidence cohort","volume":"22","author":"Andersen","year":"2015","journal-title":"European Journal of Neurology"},{"issue":"Suppl 2","key":"10.1002\/14651858.CD013606.pub2-BIB0105|cit105","first-page":"697","article-title":"Minimum clinically important difference for brain atrophy measures in multiple sclerosis","volume":"25","author":"Azevedo","year":"2019","journal-title":"Multiple Sclerosis Journal"},{"issue":"Pt 11","key":"10.1002\/14651858.CD013606.pub2-BIB0106|cit106","doi-asserted-by":"publisher","first-page":"2059","DOI":"10.1093\/brain\/120.11.2059","article-title":"Comparison of MRI criteria at first presentation to predict conversion to clinically definite multiple sclerosis","volume":"120","author":"Barkhof","year":"1997","journal-title":"Brain"},{"issue":"2","key":"10.1002\/14651858.CD013606.pub2-BIB0107|cit107","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1191\/135248506ms1263oa","article-title":"Axonal damage markers in the cerebrospinal fluid of patients with clinically isolated syndrome improve predicting conversion to definite multiple sclerosis","volume":"12","author":"Brettschneider","year":"2006","journal-title":"Multiple Sclerosis Journal"},{"issue":"5","key":"10.1002\/14651858.CD013606.pub2-BIB0108|cit108","doi-asserted-by":"crossref","first-page":"1609","DOI":"10.1111\/ene.14705","article-title":"Quantifying the risk of disease reactivation after interferon and glatiramer acetate discontinuation in multiple sclerosis: the VIAADISC score","volume":"28","author":"Bsteh","year":"2021","journal-title":"European Journal of Neurology"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0109|cit109","first-page":"198","article-title":"Prediction of conversion to secondary progression phase in multiple sclerosis","volume":"23","author":"Castellaro","year":"2015","journal-title":"Multiple Sclerosis Journal"},{"issue":"20","key":"10.1002\/14651858.CD013606.pub2-BIB0110|cit110","doi-asserted-by":"crossref","first-page":"4362","DOI":"10.1002\/sim.9034","article-title":"A two-stage prediction model for heterogeneous effects of treatments","volume":"40","author":"Chalkou","year":"2021","journal-title":"Statistics in Medicine"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0111|cit111","first-page":"62","article-title":"Prognostic factors for multiple sclerosis in patients with spinal isolated syndromes","volume":"24","author":"Costa","year":"2017","journal-title":"European Journal of Neurology"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0112|cit112","first-page":"112","article-title":"Indirect comparison of glatiramer acetate 40mg\/mL TIW and 20mg\/mL QD dosing regimen effects on relapse rate: results of a predictive statistical model","volume":"20","author":"Cutter","year":"2014","journal-title":"Multiple Sclerosis Journal"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0113|cit113","doi-asserted-by":"crossref","unstructured":"Damasceno A Pimentel-Silva LR Damasceno BP Cendes F Cognitive trajectories in relapsing\u2013remitting multiple sclerosis: a longitudinal 6-year study 35th European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS); 2019 September 11-13; Stockholm (Sweden) ECTRIMS","DOI":"10.1177\/1352458519878685"},{"issue":"13","key":"10.1002\/14651858.CD013606.pub2-BIB0114|cit114","doi-asserted-by":"publisher","first-page":"1740","DOI":"10.1177\/1352458519878685","article-title":"Cognitive trajectories in relapsing\u2013remitting multiple sclerosis: a longitudinal 6-year study","volume":"26","author":"Damasceno","year":"2019","journal-title":"Multiple Sclerosis Journal"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0115|cit115","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1186\/1472-6947-7-11","article-title":"Prognosis of the individual course of disease - steps in developing a decision support tool for multiple sclerosis","volume":"7","author":"Daumer","year":"2007","journal-title":"BMC Medical Informatics and Decision Making"},{"issue":"6","key":"10.1002\/14651858.CD013606.pub2-BIB0116|cit116","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1111\/ene.13904","article-title":"Predicting clinical progression in multiple sclerosis after 6 and 12 years","volume":"26","author":"Dekker","year":"2019","journal-title":"European Journal of Neurology"},{"issue":"12","key":"10.1002\/14651858.CD013606.pub2-BIB0117|cit117","doi-asserted-by":"crossref","first-page":"e245","DOI":"10.1016\/j.ijmedinf.2011.09.003","article-title":"An evolutionary-fuzzy DSS for assessing health status in multiple sclerosis disease","volume":"80","author":"Esposito","year":"2011","journal-title":"International Journal of Medical Informatics"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0118|cit118","first-page":"S42","article-title":"Intracortical lesions and new magnetic resonance imaging diagnostic criteria for multiple sclerosis","volume":"16","author":"Filippi","year":"2010","journal-title":"Multiple Sclerosis Journal"},{"issue":"20","key":"10.1002\/14651858.CD013606.pub2-BIB0119|cit119","doi-asserted-by":"crossref","first-page":"1759","DOI":"10.1212\/01.wnl.0000435551.90824.d0","article-title":"Gray matter damage predicts the accumulation of disability 13 years later in MS","volume":"81","author":"Filippi","year":"2013","journal-title":"Neurology"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0120|cit120","doi-asserted-by":"crossref","first-page":"102705","DOI":"10.1016\/j.nicl.2021.102705","article-title":"Quantifying disease pathology and predicting disease progression in multiple sclerosis with only clinical routine T2-FLAIR MRI","volume":"31","author":"Fuchs","year":"2021","journal-title":"NeuroImage: Clinical"},{"issue":"8","key":"10.1002\/14651858.CD013606.pub2-BIB0121|cit121","doi-asserted-by":"crossref","first-page":"2533","DOI":"10.1111\/ene.14845","article-title":"Scoring the 10-year risk of ambulatory disability in multiple sclerosis: the RoAD score","volume":"28","author":"Gasperini","year":"2021","journal-title":"European Journal of Neurology"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0122|cit122","unstructured":"Gasperini C Prosperini L Tortorella C Haggiag S Ruggieri S Mancinelli CR Scoring the 10-year risk of ambulatory disability in DMD treated multiple sclerosis patients: the RoAD score 34th European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS); 2018 October 10-12; Berlin (Germany) ECTRIMS"},{"issue":"Suppl 2","key":"10.1002\/14651858.CD013606.pub2-BIB0123|cit123","first-page":"58","article-title":"Scoring the 10-year risk of ambulatory disability in DMD treated multiple sclerosis patients: the RoAD score","volume":"24","author":"Gasperini","year":"2018","journal-title":"Multiple Sclerosis Journal"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0124|cit124","first-page":"341","article-title":"Application of image and signal processing algorithms to oligoclonal IgG bands classification","volume":"16","author":"Gomez-Gonzalez","year":"2010","journal-title":"Multiple Sclerosis Journal"},{"issue":"5","key":"10.1002\/14651858.CD013606.pub2-BIB0125|cit125","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1111\/ene.13274","article-title":"Neurofilament light chain in cerebrospinal fluid and prediction of disease activity in clinically isolated syndrome and relapsing-remitting multiple sclerosis","volume":"24","author":"Hakansson","year":"2017","journal-title":"European Journal of Neurology"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0126|cit126","doi-asserted-by":"crossref","unstructured":"Ho J Ghosh J Unnikrishnan K Risk prediction of a multiple sclerosis diagnosis 2013 IEEE International Conference on Healthcare Informatics 2013 175 83","DOI":"10.1109\/ICHI.2013.31"},{"issue":"Suppl 2","key":"10.1002\/14651858.CD013606.pub2-BIB0127|cit127","first-page":"788","article-title":"Predictors of long term disability progression in patients with relapsing remitting multiple sclerosis","volume":"24","author":"Ignatova","year":"2018","journal-title":"Multiple Sclerosis Journal"},{"issue":"11","key":"10.1002\/14651858.CD013606.pub2-BIB0128|cit128","doi-asserted-by":"crossref","first-page":"1933","DOI":"10.1007\/s00415-011-6033-x","article-title":"Prognostic value of multimodal evoked potentials in multiple sclerosis: the EP score","volume":"258","author":"Invernizzi","year":"2011","journal-title":"Journal of Neurology"},{"issue":"1","key":"10.1002\/14651858.CD013606.pub2-BIB0129|cit129","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1111\/ahg.12342","article-title":"Genetic model of MS severity predicts future accumulation of disability","volume":"84","author":"Jackson","year":"2020","journal-title":"Annals of Human Genetics"},{"issue":"4","key":"10.1002\/14651858.CD013606.pub2-BIB0130|cit130","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1038\/gene.2013.17","article-title":"Multiple sclerosis susceptibility loci do not alter clinical and MRI outcomes in clinically isolated syndrome","volume":"14","author":"Kalincik","year":"2013","journal-title":"Genes & Immunity"},{"issue":"Suppl 16","key":"10.1002\/14651858.CD013606.pub2-BIB0131|cit131","doi-asserted-by":"crossref","first-page":"P4.386","DOI":"10.1212\/WNL.88.16_supplement.P4.386","article-title":"Multimodal EPs predict no evidence of disease activity at two years of first line multiple sclerosis treatment","volume":"88","author":"Leocani","year":"2017","journal-title":"Neurology"},{"issue":"10","key":"10.1002\/14651858.CD013606.pub2-BIB0132|cit132","doi-asserted-by":"crossref","first-page":"2893","DOI":"10.1007\/s10072-020-04395-5","article-title":"Early putamen hypertrophy and ongoing hippocampus atrophy predict cognitive performance in the first ten years of relapsing-remitting multiple sclerosis","volume":"41","author":"Morelli","year":"2020","journal-title":"Neurological Sciences"},{"issue":"Suppl 1","key":"10.1002\/14651858.CD013606.pub2-BIB0133|cit133","first-page":"339","article-title":"Modelling natural history for the UK multiple sclerosis risk-sharing scheme","volume":"19","author":"Palace","year":"2013","journal-title":"Multiple Sclerosis Journal"},{"issue":"3","key":"10.1002\/14651858.CD013606.pub2-BIB0134|cit134","doi-asserted-by":"crossref","first-page":"586","DOI":"10.3390\/cells9030586","article-title":"The potential of computational modeling to predict disease course and treatment response in patients with relapsing multiple sclerosis","volume":"9","author":"Pappalardo","year":"2020","journal-title":"Cells"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0135|cit135","first-page":"15","article-title":"Biomarkes for diagnosis and prognosis in multiple sclerosis","volume":"24","author":"Petrou","year":"2018","journal-title":"Multiple Sclerosis Journal"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0136|cit136","doi-asserted-by":"crossref","unstructured":"Preziosa P Rocca M Mesaros S Copetti M Petrolini M Drulovic J Different MRI measures predict clinical deterioration and cognitive impairment in MS: a 5 year longitudinal study 31st European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS); 2015 October 7-10; Barcelona (Spain) ECTRIMS","DOI":"10.1212\/WNL.84.14_supplement.P6.142"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0137|cit137","first-page":"856","article-title":"High neurofilament light chain and high quinolinic acid levels in the CSF of patients with multiple sclerosis are independent predictors of active, disabling disease","volume":"25","author":"Rajda","year":"2019","journal-title":"Multiple Sclerosis Journal"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0138|cit138","unstructured":"Rio J Rovira A Gasperini C Tintore M Prosperini L Otero-Romero S Treatment response scoring systems to assess long term prognosis in relapsing-remitting multiple sclerosis patients 35th European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS); 2019 September 11-13; Stockholm (Sweden) ECTRIMS"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0139|cit139","first-page":"121","article-title":"Treatment response scoring systems to assess long term prognosis in relapsing-remitting multiple sclerosis patients","volume":"25","author":"Rio","year":"2019","journal-title":"Multiple Sclerosis Journal"},{"issue":"6","key":"10.1002\/14651858.CD013606.pub2-BIB0140|cit140","doi-asserted-by":"publisher","first-page":"1705","DOI":"10.1109\/TSMCC.2012.2217326","article-title":"Using multidimensional bayesian network classifiers to assist the treatment of multiple sclerosis","volume":"42","author":"Rodriguez","year":"2012","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0141|cit141","unstructured":"Rothman AM Button J Balcer LJ Frohman EM Frohman TC Reich DS Retinal measurements predict 10-year disability in multiple sclerosis 32nd European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS); 2016 September 14-17; London (UK) ECTRIMS"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0142|cit142","first-page":"20","article-title":"Retinal measurements predict 10-year disability in multiple sclerosis","volume":"22","author":"Rothman","year":"2016","journal-title":"Multiple Sclerosis Journal"},{"issue":"2","key":"10.1002\/14651858.CD013606.pub2-BIB0143|cit143","first-page":"210","article-title":"Brain complexity and damage in patients with multiple sclerosis using fractal analysis: a new imaging outcome for monitoring MS severity","volume":"24","author":"Roura","year":"2018","journal-title":"Multiple Sclerosis Journal"},{"issue":"12","key":"10.1002\/14651858.CD013606.pub2-BIB0144|cit144","doi-asserted-by":"crossref","first-page":"1432","DOI":"10.1177\/1352458511414602","article-title":"Pronounced focal and diffuse brain damage predicts short-term disease evolution in patients with clinically isolated syndrome suggestive of multiple sclerosis","volume":"17","author":"Sbardella","year":"2011","journal-title":"Multiple Sclerosis Journal"},{"issue":"1","key":"10.1002\/14651858.CD013606.pub2-BIB0145|cit145","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1177\/1352458511416836","article-title":"Prediction of long-term disability in multiple sclerosis","volume":"18","author":"Schlaeger","year":"2012","journal-title":"Multiple Sclerosis Journal"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0146|cit146","unstructured":"Srinivasan J Gudesblatt M Multiple sclerosis management: predicting disease trajectory of multiple sclerosis on multi-dimensional data including digital cognitive assessments and patient reported outcomes using machine learning techniques 5th Annual Americas Committee for Treatment and Research in Multiple Sclerosis (ACTRIMS); 2020 February 27-29; West Palm Beach (FL) West Palm Beach (FL) ACTRIMS 2020"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0147|cit147","first-page":"56","article-title":"Predicting MS extremes: benign and aggressive","volume":"23","author":"Tintor\u00e9","year":"2015","journal-title":"Multiple Sclerosis"},{"issue":"9","key":"10.1002\/14651858.CD013606.pub2-BIB0148|cit148","doi-asserted-by":"crossref","first-page":"1306","DOI":"10.1177\/1352458518790397","article-title":"Predicting the profile of increasing disability in multiple sclerosis","volume":"25","author":"Tomassini","year":"2019","journal-title":"Multiple Sclerosis Journal"},{"issue":"1","key":"10.1002\/14651858.CD013606.pub2-BIB0149|cit149","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/2043-9113-3-18","article-title":"Using biomarkers to predict progression from clinically isolated syndrome to multiple sclerosis","volume":"3","author":"Tossberg","year":"2013","journal-title":"Journal of Clinical Bioinformatics"},{"issue":"1","key":"10.1002\/14651858.CD013606.pub2-BIB0150|cit150","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1177\/1352458516642314","article-title":"Combining clinical and magnetic resonance imaging markers enhances prediction of 12-year disability in multiple sclerosis","volume":"23","author":"Uher","year":"2017","journal-title":"Multiple Sclerosis Journal"},{"issue":"2","key":"10.1002\/14651858.CD013606.pub2-BIB0151|cit151","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1111\/ene.13200","article-title":"Identification of multiple sclerosis patients at highest risk of cognitive impairment using an integrated brain magnetic resonance imaging assessment approach","volume":"24","author":"Uher","year":"2017","journal-title":"European Journal of Neurology"},{"issue":"5","key":"10.1002\/14651858.CD013606.pub2-BIB0152|cit152","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1016\/j.msard.2014.04.005","article-title":"A web-based decision support tool for prognosis simulation in multiple sclerosis","volume":"3","author":"Veloso","year":"2014","journal-title":"Multiple Sclerosis and Related Disorders"},{"issue":"3","key":"10.1002\/14651858.CD013606.pub2-BIB0153|cit153","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.clineuro.2005.11.016","article-title":"Pregnancy and multiple sclerosis: the children of PRIMS","volume":"108","author":"Vukusic","year":"2006","journal-title":"Clinical Neurology and Neurosurgery"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0154|cit154","first-page":"112","article-title":"Prediction of disability and treatment response from radiomic features: a machine learning analysis from the combirx multi-center cohort","volume":"25","author":"Wahid","year":"2019","journal-title":"Multiple Sclerosis Journal"},{"issue":"5","key":"10.1002\/14651858.CD013606.pub2-BIB0155|cit155","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1177\/1352458508101951","article-title":"Serum IgG repertoire in clinically isolated syndrome predicts multiple sclerosis","volume":"15","author":"Zephir","year":"2009","journal-title":"Multiple Sclerosis Journal"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0156|cit156","first-page":"872","article-title":"Validation of the scoring algorithm for a novel integrative MS progression discussion tool","volume":"26","author":"Ziemssen","year":"2019","journal-title":"European Journal of Neurology"},{"issue":"2","key":"10.1002\/14651858.CD013606.pub2-BIB0157|cit157","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1111\/j.1365-2249.2007.03405.x","article-title":"Zinc-ion binding and cytokine activity regulation pathways predicts outcome in relapsing-remitting multiple sclerosis","volume":"149","author":"Achiron","year":"2007","journal-title":"Clinical and Experimental Immunology"},{"issue":"Suppl 2","key":"10.1002\/14651858.CD013606.pub2-BIB0158|cit158","first-page":"130","article-title":"Predicting relapse episodes in patients with multiple sclerosis treated with disease modifying therapies in a large representative real-world cohort in the United States","volume":"28","author":"Behling","year":"2019","journal-title":"Pharmacoepidemiology and Drug Safety"},{"issue":"Suppl 2","key":"10.1002\/14651858.CD013606.pub2-BIB0159|cit159","first-page":"686","article-title":"A clinical decision system based on resting state fMRI-derived features to predict the conversion of CIS to RRMS","volume":"25","author":"Castellazzi","year":"2019","journal-title":"Multiple Sclerosis Journal"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0160|cit160","unstructured":"Castellazzi G Martinelli D Collorone S Alhamadi A Debernard L Melzer TR A clinical decision system based on resting state fMRI-derived features to predict the conversion of CIS to RRMS 35th European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS); 2019 September 11-13; Stockholm (Sweden) onlinelibrary.ectrims-congress.eu\/ectrims\/2019\/stockholm\/278467 ECTRIMS 2019"},{"issue":"Suppl 15","key":"10.1002\/14651858.CD013606.pub2-BIB0161|cit161","first-page":"P5.2","article-title":"Predicting EDSS in MS through imaging biomarkers using artificial neural networks","volume":"92","author":"Chaar","year":"2019","journal-title":"Neurology"},{"issue":"Suppl 1","key":"10.1002\/14651858.CD013606.pub2-BIB0162|cit162","first-page":"170","article-title":"Artificial intelligence techniques in the diagnosis of clinically definite multiple sclerosis","volume":"20","author":"Dalla Costa","year":"2014","journal-title":"Multiple Sclerosis Journal"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0163|cit163","first-page":"S157","article-title":"Joint modelling of multivariate longitudinal data for mixed responses and survival in multiple sclerosis","volume":"15","author":"Ghosh","year":"2009","journal-title":"Multiple Sclerosis"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0164|cit164","unstructured":"Kister I Bacon T Levinas M Green R Cutter G Chamot E Stability and prognostic utility of patient-derived MS severity score (P-MSSS) among MS clinic patients 31st European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS); 2015 October 7-10; Barcelona (Spain) ECTRIMS"},{"issue":"Suppl 11","key":"10.1002\/14651858.CD013606.pub2-BIB0165|cit165","first-page":"410","article-title":"Stability and prognostic utility of patient-derived MS severity score (P-MSSS) among MS clinic patients","volume":"21","author":"Kister","year":"2015","journal-title":"Multiple Sclerosis Journal"},{"issue":"Suppl 14","key":"10.1002\/14651858.CD013606.pub2-BIB0166|cit166","doi-asserted-by":"crossref","first-page":"P3.214","DOI":"10.1212\/WNL.84.14_supplement.P3.214","article-title":"Novel, easy-to-use prediction tool accurately estimates probability of \u201caggressive MS\u201d at 2-year follow up","volume":"84","author":"Kister","year":"2015","journal-title":"Neurology"},{"issue":"Suppl 2","key":"10.1002\/14651858.CD013606.pub2-BIB0167|cit167","first-page":"405","article-title":"The RECIS (risk estimate in CIS) study: a novel model to early predict clinically isolated syndrome evolution","volume":"25","author":"Mallucci","year":"2019","journal-title":"Multiple Sclerosis Journal"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0168|cit168","doi-asserted-by":"crossref","unstructured":"Medin J Joyeux A Braune S Bergmann A Rigg J Wang L Predicting disease activity for patients with relapsing remitting multiple sclerosis using electronic medical records American Academy of Neurology Annual Meeting; 2016 April 15-21; Vancouver (Canada)","DOI":"10.1212\/WNL.86.16_supplement.P1.395"},{"issue":"Suppl 16","key":"10.1002\/14651858.CD013606.pub2-BIB0169|cit169","doi-asserted-by":"crossref","first-page":"P1.395","DOI":"10.1212\/WNL.86.16_supplement.P1.395","article-title":"Predicting disease activity for patients with relapsing remitting multiple sclerosis using electronic medical records","volume":"86","author":"Medin","year":"2016","journal-title":"Neurology"},{"issue":"Suppl 3","key":"10.1002\/14651858.CD013606.pub2-BIB0170|cit170","first-page":"231","article-title":"Pattern recognition for neuroimaging toolbox PRoNTo: a pilot study in predicting clinically isolated syndrome conversion","volume":"23","author":"Pareto","year":"2017","journal-title":"Multiple Sclerosis Journal"},{"issue":"Suppl 15","key":"10.1002\/14651858.CD013606.pub2-BIB0171|cit171","doi-asserted-by":"crossref","first-page":"2002","DOI":"10.1212\/WNL.94.15_supplement.2002","article-title":"Predicting long-term sustained disability progression in multiple sclerosis","volume":"94","author":"Sharmin","year":"2020","journal-title":"Neurology"},{"issue":"Suppl 3","key":"10.1002\/14651858.CD013606.pub2-BIB0172|cit172","first-page":"181","article-title":"Predicting long-term sustained disability progression in multiple sclerosis: application in the clarity trial","volume":"26","author":"Sharmin","year":"2020","journal-title":"Multiple Sclerosis Journal"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0173|cit173","unstructured":"Sharmin S Malpas C Horakova D Havrdova EK Izquierdo G Eichau S Predicting long-term sustained disability progression in multiple sclerosis 35th European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS); 2019 September 11-13; Stockholm (Sweden) ECTRIMS"},{"issue":"Suppl 2","key":"10.1002\/14651858.CD013606.pub2-BIB0174|cit174","first-page":"119","article-title":"Predicting long-term sustained disability progression in multiple sclerosis","volume":"25","author":"Sharmin","year":"2019","journal-title":"Multiple Sclerosis Journal"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0175|cit175","unstructured":"Sharmin S Follow-up for cochrane review - prognostc predicton models in multiple sclerosis [pers comm] Email to: On BI"},{"issue":"16","key":"10.1002\/14651858.CD013606.pub2-BIB0176|cit176","doi-asserted-by":"crossref","first-page":"P1.368","DOI":"10.1212\/WNL.88.16_supplement.P1.368","article-title":"Multiple sclerosis care optimization tool (MS-COT): a clinical application prototype to predict future disease activity","volume":"88","author":"Silva","year":"2017","journal-title":"Neurology"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0177|cit177","unstructured":"Silva D Meier DP Ritter S Tomic D Medin J Lange M Multiple sclerosis care optimization tool (MSCOT): a clinical application prototype to predict future disease activity 69th Congress of the American Academy of Neurology; 2017 April 22-28; Boston (MA) Novartis Pharma AG"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0178|cit178","unstructured":"Tam R Follow-up for cochrane review - prognostic prediction models in multiple sclerosis (Tayyab 2020) [pers comm] Email to: K Reeve"},{"issue":"Suppl 3","key":"10.1002\/14651858.CD013606.pub2-BIB0179|cit179","first-page":"116","article-title":"Machine learning of deep grey matter volumes on mri for predicting new disease activity after a first clinical demyelinating event","volume":"26","author":"Tayyab","year":"2020","journal-title":"Multiple Sclerosis Journal"},{"issue":"9","key":"10.1002\/14651858.CD013606.pub2-BIB0180|cit180","first-page":"S163","article-title":"Comparison of model-based and matching-based prediction of the annualised relapse-rate of MS-patients","volume":"15","author":"Thiele","year":"2009","journal-title":"Multiple Sclerosis"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0181|cit181","unstructured":"Tintor\u00e9 M R\u00edo J Otero-Romero S Arrambide G Tur C Comabella M Dynamic model for predicting prognosis in CIS patients 31st European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS); 2015 October 7-10; Barcelona (Spain) ECTRIMS"},{"issue":"Suppl 11","key":"10.1002\/14651858.CD013606.pub2-BIB0182|cit182","first-page":"33","article-title":"Dynamic model for predicting prognosis in CIS patients","volume":"21","author":"Tintor\u00e9","year":"2015","journal-title":"Multiple Sclerosis Journal"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0183|cit183","unstructured":"Tommasin S Taloni A Farrelly FA Petsas N Ruggieri S Gianni C Evaluation of 5-year disease progression in multiple sclerosis via magnetic-resonance-based deep learning techniques 35th European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS); 2019 September 11-13; Stockholm (Sweden) ECTRIMS"},{"issue":"Suppl 2","key":"10.1002\/14651858.CD013606.pub2-BIB0184|cit184","first-page":"468","article-title":"Evaluation of 5-year disease progression in multiple sclerosis via magnetic-resonance-based deep learning techniques","volume":"25","author":"Tommasin","year":"2019","journal-title":"Multiple Sclerosis Journal"},{"issue":"Suppl 1","key":"10.1002\/14651858.CD013606.pub2-BIB0185|cit185","first-page":"71","article-title":"Radiomic prediction of clinical outcome in multiple sclerosis patients from the combirx cohort","volume":"24","author":"Wahid","year":"2018","journal-title":"Multiple Sclerosis Journal"},{"issue":"5","key":"10.1002\/14651858.CD013606.pub2-BIB0186|cit186","doi-asserted-by":"publisher","first-page":"639","DOI":"10.3111\/13696998.2013.778268","article-title":"The cost burden of multiple sclerosis in the United States: a systematic review of the literature","volume":"16","author":"Adelman","year":"2013","journal-title":"Journal of Medical Economics"},{"issue":"4","key":"10.1002\/14651858.CD013606.pub2-BIB0187|cit187","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1002\/(SICI)1097-0258(20000229)19:4&lt;453::AID-SIM350&gt;3.0.CO;2-5","article-title":"What do we mean by validating a prognostic model?","volume":"19","author":"Altman","year":"2000","journal-title":"Statistics in Medicine"},{"issue":"4","key":"10.1002\/14651858.CD013606.pub2-BIB0188|cit188","doi-asserted-by":"publisher","first-page":"580","DOI":"10.1373\/clinchem.2013.220335","article-title":"The time has come to register diagnostic and prognostic research","volume":"60","author":"Altman","year":"2014","journal-title":"Clinical Chemistry"},{"issue":"12","key":"10.1002\/14651858.CD013606.pub2-BIB0189|cit189","doi-asserted-by":"publisher","first-page":"734","DOI":"10.1038\/s41577-022-00718-z","article-title":"The immunology of multiple sclerosis","volume":"22","author":"Attfield","year":"2022","journal-title":"Nature Reviews Immunology"},{"issue":"4 Suppl","key":"10.1002\/14651858.CD013606.pub2-BIB0190|cit190","first-page":"30s","article-title":"Measurement of brain and spinal cord atrophy by magnetic resonance imaging as a tool to monitor multiple sclerosis","volume":"15","author":"Bakshi","year":"2005","journal-title":"Journal of Neuroimaging"},{"issue":"3","key":"10.1002\/14651858.CD013606.pub2-BIB0191|cit191","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1177\/1352458517690270","article-title":"Environmental risk factors and multiple sclerosis: an umbrella review of systematic reviews and meta-analyses","volume":"14","author":"Belbasis","year":"2015","journal-title":"Lancet Neurology"},{"issue":"3","key":"10.1002\/14651858.CD013606.pub2-BIB0192|cit192","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1038\/s41582-023-00775-5","article-title":"Epstein\u2013Barr virus as a leading cause of multiple sclerosis: mechanisms and implications","volume":"19","author":"Bjornevik","year":"2023","journal-title":"Nature Reviews Neurology"},{"issue":"3","key":"10.1002\/14651858.CD013606.pub2-BIB0193|cit193","doi-asserted-by":"publisher","first-page":"487\u201389","DOI":"10.1148\/radiol.2019192515","article-title":"Assessing radiology research on artificial intelligence: a brief guide for authors, reviewers, and readers\u2014from the Radiology editorial board","volume":"294","author":"Bluemke","year":"2020","journal-title":"Radiology"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0194|cit194","doi-asserted-by":"publisher","first-page":"h5527","DOI":"10.1136\/bmj.h5527","article-title":"STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies","volume":"351","author":"Bossuyt","year":"2015","journal-title":"BMJ"},{"issue":"5","key":"10.1002\/14651858.CD013606.pub2-BIB0195|cit195","doi-asserted-by":"publisher","first-page":"1314","DOI":"10.1002\/bimj.201700243","article-title":"Making complex prediction rules applicable for readers: current practice in random forest literature and recommendations","volume":"61","author":"Boulesteix","year":"2019","journal-title":"Biometrical Journal"},{"issue":"5","key":"10.1002\/14651858.CD013606.pub2-BIB0196|cit196","doi-asserted-by":"publisher","first-page":"e1001221","DOI":"10.1371\/journal.pmed.1001221","article-title":"Reporting and methods in clinical prediction research: a systematic review","volume":"9","author":"Bouwmeester","year":"2012","journal-title":"PLOS Medicine"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0197|cit197","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1186\/s12916-019-1345-2","article-title":"Defining responders to therapies by astatistical modeling approach appliedto randomized clinical trial data","volume":"17","author":"Bovis","year":"2019","journal-title":"BMC Medicine"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0198|cit198","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.msard.2019.01.045","article-title":"Prognostic factors of disability in relapsing remitting multiple sclerosis","volume":"30","author":"Briggs","year":"2019","journal-title":"Multiple Sclerosis and Related Disorders"},{"issue":"2","key":"10.1002\/14651858.CD013606.pub2-BIB0199|cit199","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1002\/jrsm.1355","article-title":"Conduct and reporting of citation searching in Cochrane systematic reviews: a cross-sectional study","volume":"11","author":"Briscoe","year":"2020","journal-title":"Research Synthesis Methods"},{"issue":"5","key":"10.1002\/14651858.CD013606.pub2-BIB0200|cit200","doi-asserted-by":"publisher","first-page":"e0233575","DOI":"10.1371\/journal.pone.0233575","article-title":"Systematic review of prediction models in relapsing remitting multiple sclerosis","volume":"15","author":"Brown","year":"2020","journal-title":"PLOS One"},{"issue":"3","key":"10.1002\/14651858.CD013606.pub2-BIB0201|cit201","doi-asserted-by":"crossref","first-page":"419","DOI":"10.2307\/2983440","article-title":"Model uncertainty, data mining and statistical inference","volume":"158","author":"Chatfield","year":"1995","journal-title":"Journal of the Royal Statistical Society. Series A (Statistics in Society)"},{"issue":"26","key":"10.1002\/14651858.CD013606.pub2-BIB0202|cit202","doi-asserted-by":"publisher","first-page":"2507","DOI":"10.1056\/NEJMp1702071","article-title":"Machine learning and prediction in medicine \u2014 beyond the peak of inflated expectations","volume":"376","author":"Chen","year":"2017","journal-title":"New England Journal of Medicine"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0203|cit203","unstructured":"Cochrane Multiple Sclerosis and Rare Disease of the CNS Our reviews msrdcns.cochrane.org\/our-review"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0204|cit204","volume-title":"Statistical Power Analysis for the Behavioral Sciences","author":"Cohen","year":"1988"},{"issue":"2","key":"10.1002\/14651858.CD013606.pub2-BIB0205|cit205","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.jclinepi.2014.11.010","article-title":"Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement","volume":"68","author":"Collins","year":"2015","journal-title":"Journal of Clinical Epidemiology"},{"issue":"3","key":"10.1002\/14651858.CD013606.pub2-BIB0206|cit206","doi-asserted-by":"publisher","first-page":"201","DOI":"10.7326\/0003-4819-118-3-199302010-00009","article-title":"The risk of determining risk with multivariable models","volume":"118","author":"Concato","year":"1993","journal-title":"Annals of Internal Medicine"},{"issue":"5","key":"10.1002\/14651858.CD013606.pub2-BIB0207|cit207","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1007\/s11910-012-0292-5","article-title":"Benign multiple sclerosis: does it exist?","volume":"12","author":"Correale","year":"2012","journal-title":"Current Neurology and Neuroscience Reports"},{"issue":"4","key":"10.1002\/14651858.CD013606.pub2-BIB0208|cit208","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1002\/ana.24747","article-title":"Long-term evolution of multiple sclerosis disability in the treatment era","volume":"80","author":"Cree","year":"2016","journal-title":"Annals of Neurology"},{"issue":"5","key":"10.1002\/14651858.CD013606.pub2-BIB0209|cit209","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1002\/ana.25463","article-title":"Silent progression in disease activity-free relapsing multiple sclerosis","volume":"85","author":"Cree","year":"2019","journal-title":"Annals of Neurology"},{"issue":"3","key":"10.1002\/14651858.CD013606.pub2-BIB0210|cit210","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1212\/CPJ.0000000000000449","article-title":"Identifying priority outcomes that influence selection of disease-modifying therapies in MS","volume":"8","author":"Day","year":"2018","journal-title":"Neurology Clinical Practice"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0211|cit211","doi-asserted-by":"publisher","first-page":"i6460","DOI":"10.1136\/bmj.i6460","article-title":"A guide to systematic review and meta-analysis of prediction model performance","volume":"356","author":"Debray","year":"2017","journal-title":"BMJ"},{"issue":"9","key":"10.1002\/14651858.CD013606.pub2-BIB0212|cit212","doi-asserted-by":"publisher","first-page":"2768","DOI":"10.1177\/0962280218785504","article-title":"A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes","volume":"28","author":"Debray","year":"2019","journal-title":"Statistical Methods in Medical Research"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0213|cit213","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1186\/1741-7015-10-116","article-title":"Personalized medicine in multiple sclerosis: hope or reality?","volume":"10","author":"Derfuss","year":"2012","journal-title":"BMC Medicine"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0214|cit214","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.jclinepi.2021.06.024","article-title":"Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved","volume":"138","author":"Dhiman","year":"2021","journal-title":"Journal of Clinical Epidemiology"},{"issue":"3","key":"10.1002\/14651858.CD013606.pub2-BIB0215|cit215","doi-asserted-by":"publisher","first-page":"A12","DOI":"10.1016\/0735-1097(89)90157-5","article-title":"Future imperfect: the limitations of clinical prediction models and the limits of clinical prediction","volume":"14","author":"Diamond","year":"1989","journal-title":"Journal of the American College of Cardiology"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0216|cit216","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.msard.2019.01.039","article-title":"Highly active multiple sclerosis: an update","volume":"30","author":"Diaz","year":"2019","journal-title":"Multiple Sclerosis and Related Disorders"},{"issue":"10","key":"10.1002\/14651858.CD013606.pub2-BIB0217|cit217","doi-asserted-by":"publisher","first-page":"3626","DOI":"10.1007\/s00415-020-09964-4","article-title":"Early diagnosis of secondary progressive multiple sclerosis: focus on fluid and neurophysiological biomarkers","volume":"268","author":"Ferrazzano","year":"2021","journal-title":"Journal of Neurology"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0218|cit218","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.jclinepi.2019.12.023","article-title":"GRADE Guidelines 28: Use of GRADE for the assessment of evidence about prognostic factors: rating certainty in identification of groups of patients with different absolute risks","volume":"121","author":"Foroutan","year":"2020","journal-title":"Journal of Clinical Epidemiology"},{"issue":"3","key":"10.1002\/14651858.CD013606.pub2-BIB0219|cit219","doi-asserted-by":"publisher","first-page":"761","DOI":"10.1212\/CON.0000000000000331","article-title":"Severe, highly active, or aggressive multiple sclerosis","volume":"22","author":"Freedman","year":"2016","journal-title":"Continuum"},{"issue":"3","key":"10.1002\/14651858.CD013606.pub2-BIB0220|cit220","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1177\/1352458516672017","article-title":"Personalised medicine for multiple sclerosis care","volume":"23","author":"Gafson","year":"2017","journal-title":"Multiple Sclerosis Journal"},{"issue":"24","key":"10.1002\/14651858.CD013606.pub2-BIB0221|cit221","doi-asserted-by":"publisher","first-page":"2059","DOI":"10.1212\/01.wnl.0000264890.97479.b1.","article-title":"Predicting short-term disability in multiple sclerosis","volume":"68","author":"Gauthier","year":"2007","journal-title":"Neurology"},{"issue":"6","key":"10.1002\/14651858.CD013606.pub2-BIB0222|cit222","first-page":"1165","article-title":"Multiple sclerosis: the role of MR imaging","volume":"27","author":"Ge","year":"2006","journal-title":"American Journal of Neuroradiology"},{"issue":"2","key":"10.1002\/14651858.CD013606.pub2-BIB0223|cit223","doi-asserted-by":"publisher","first-page":"e32844","DOI":"10.1371\/journal.pone.0032844","article-title":"Search filters for finding prognostic and diagnostic prediction studies in MEDLINE to enhance systematic reviews","volume":"7","author":"Bouwmeester","year":"2012","journal-title":"PLOS One"},{"issue":"1","key":"10.1002\/14651858.CD013606.pub2-BIB0224|cit224","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","article-title":"The meaning and use of the area under a receiver operating characteristic (ROC) curve","volume":"143","author":"Hanley","year":"1982","journal-title":"Radiology"},{"issue":"4","key":"10.1002\/14651858.CD013606.pub2-BIB0225|cit225","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1002\/(SICI)1097-0258(19960229)15:4&lt;361::AID-SIM168&gt;3.0.CO;2-4","article-title":"Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors","volume":"15","author":"Harrell","year":"1996","journal-title":"Statistics in Medicine"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0226|cit226","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4757-3462-1","volume-title":"Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis","author":"Harrell","year":"2001"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0227|cit227","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The Elements of Statistical Learning","author":"Hastie","year":"2009"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0228|cit228","doi-asserted-by":"publisher","first-page":"101928","DOI":"10.1016\/j.msard.2020.101928","article-title":"Predictive medicine in multiple sclerosis: a systematic review","volume":"40","author":"Havas","year":"2020","journal-title":"Multiple Sclerosis and Related Disorders"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0229|cit229","unstructured":"Hemmer B Diagnosis and therapy of multiple sclerosis, neuromyelitis optica spectrum diseases and MOG-IgG-associated diseases, S2k Guideline Deutsche Gesellschaft f\u00fcr Neurologie (Hrsg.), Leitlinien f\u00fcr Diagnostik und Therapie in der Neurologie. (www.dgn.org\/leitlinien)"},{"issue":"4","key":"10.1002\/14651858.CD013606.pub2-BIB0230|cit230","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1177\/1352458517690270","article-title":"A systematic review of modifiable risk factors in the progression of multiple sclerosis","volume":"23","author":"Hempel","year":"2017","journal-title":"Multiple Sclerosis Journal"},{"issue":"5","key":"10.1002\/14651858.CD013606.pub2-BIB0231|cit231","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1016\/j.jclinepi.2003.09.014","article-title":"Covariate adjustment in randomized controlled trials with dichotomous outcomes increases statistical power and reduces sample size requirements","volume":"57","author":"Hern\u00e1ndez","year":"2004","journal-title":"Journal of Clinical Epidemiology"},{"issue":"2","key":"10.1002\/14651858.CD013606.pub2-BIB0232|cit232","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/S1474-4422(15)00334-8","article-title":"The search for the target antigens of multiple sclerosis, part 1: autoreactive CD4+ T lymphocytes as pathogenic effectors and therapeutic targets","volume":"15","author":"Hohlfeld","year":"2016","journal-title":"Lancet Neurology"},{"issue":"3","key":"10.1002\/14651858.CD013606.pub2-BIB0233|cit233","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1016\/S1474-4422(15)00313-0","article-title":"The search for the target antigens of multiple sclerosis, part 2: CD8+ T cells, B cells, and antibodies in the focus of reverse-translational research","volume":"15","author":"Hohlfeld","year":"2016","journal-title":"Lancet Neurology"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0234|cit234","doi-asserted-by":"publisher","first-page":"h870","DOI":"10.1136\/bmj.h870","article-title":"Use of GRADE for assessment of evidence about prognosis: rating confidence in estimates of event rates in broad categories of patients","volume":"350","author":"Iorio","year":"2015","journal-title":"BMJ"},{"issue":"1","key":"10.1002\/14651858.CD013606.pub2-BIB0235|cit235","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1136\/qshc.2009.032953","article-title":"The hospital standardised mortality ratio: a powerful tool for Dutch hospitals to assess their quality of care?","volume":"19","author":"Jarman","year":"2010","journal-title":"BMJ Quality & Safety"},{"issue":"6","key":"10.1002\/14651858.CD013606.pub2-BIB0236|cit236","doi-asserted-by":"publisher","first-page":"515","DOI":"10.7326\/0003-4819-130-6-199903160-00016","article-title":"Assessing the generalizability of prognostic information","volume":"130","author":"Justice","year":"1999","journal-title":"Annals of Internal Medicine"},{"issue":"9","key":"10.1002\/14651858.CD013606.pub2-BIB0237|cit237","doi-asserted-by":"publisher","first-page":"2426","DOI":"10.1093\/brain\/awx185","article-title":"Towards personalized therapy for multiple sclerosis: prediction of individual treatment response","volume":"140","author":"Kalincik","year":"2017","journal-title":"Brain"},{"issue":"5","key":"10.1002\/14651858.CD013606.pub2-BIB0238|cit238","doi-asserted-by":"crossref","first-page":"e39","DOI":"10.1093\/brain\/awy056","article-title":"Reply: towards personalized therapy for multiple sclerosis: limitations of observational data","volume":"141","author":"Kalincik","year":"2018","journal-title":"Brain"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0239|cit239","doi-asserted-by":"crossref","unstructured":"Kaufman S Rosset S Perlich C Leakage in data mining: formulation, detection, and avoidance Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2011 556\u201363 10.1145\/2020408.2020496","DOI":"10.1145\/2020408.2020496"},{"issue":"3","key":"10.1002\/14651858.CD013606.pub2-BIB0240|cit240","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1002\/jrsm.1389","article-title":"Searching practices and inclusion of unpublished studies in systematic reviews of diagnostic accuracy","volume":"11","author":"Korevaar","year":"2020","journal-title":"Research Synthesis Methods"},{"issue":"7","key":"10.1002\/14651858.CD013606.pub2-BIB0241|cit241","doi-asserted-by":"publisher","DOI":"10.1002\/14651858.CD012022.pub2","article-title":"Prognostic models for newly-diagnosed chronic lymphocytic leukaemia in adults: a systematic review and meta-analysis","author":"Kreuzberger","year":"2020","journal-title":"Cochrane Database of Systematic Reviews"},{"issue":"12","key":"10.1002\/14651858.CD013606.pub2-BIB0242|cit242","doi-asserted-by":"publisher","first-page":"819","DOI":"10.1016\/0021-9681(77)90010-8","article-title":"Studies on the natural history of multiple sclerosis--8. Early prognostic features of the later course of the illness","volume":"30","author":"Kurtzke","year":"1977","journal-title":"Journal of Chronic Diseases"},{"issue":"Pt 9","key":"10.1002\/14651858.CD013606.pub2-BIB0243|cit243","doi-asserted-by":"publisher","first-page":"2395","DOI":"10.1093\/brain\/aww173","article-title":"Defining secondary progressive multiple sclerosis","volume":"139","author":"Lorscheider","year":"2016","journal-title":"Brain"},{"issue":"4","key":"10.1002\/14651858.CD013606.pub2-BIB0244|cit244","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1212\/WNL.46.4.907","article-title":"Defining the clinical course of multiple sclerosis: results of an international survey. National Multiple Sclerosis Society (USA) Advisory Committee on Clinical Trials of New Agents in Multiple Sclerosis","volume":"46","author":"Lublin","year":"1996","journal-title":"Neurology"},{"issue":"3","key":"10.1002\/14651858.CD013606.pub2-BIB0245|cit245","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1212\/WNL.0000000000000560","article-title":"Defining the clinical course of multiple sclerosis","volume":"83","author":"Lublin","year":"2014","journal-title":"Neurology"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0246|cit246","doi-asserted-by":"publisher","first-page":"554","DOI":"10.1038\/s42256-020-00239-1","article-title":"Improving the quality of machine learning in health applications and clinical research","volume":"2","author":"Mateen","year":"2020","journal-title":"Nature Machine Intelligence"},{"issue":"1","key":"10.1002\/14651858.CD013606.pub2-BIB0247|cit247","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1002\/ana.1032","article-title":"Recommended diagnostic criteria for multiple sclerosis: guidelines from the International Panel on the diagnosis of multiple sclerosis","volume":"50","author":"McDonald","year":"2001","journal-title":"Annals of Neurology"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0248|cit248","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1186\/1471-2377-14-58","article-title":"Systematic literature review and validity evaluation of the Expanded Disability Status Scale (EDSS) and the Multiple Sclerosis Functional Composite (MSFC) in patients with multiple sclerosis","volume":"14","author":"Meyer-Moock","year":"2014","journal-title":"BMC Neurology"},{"issue":"1","key":"10.1002\/14651858.CD013606.pub2-BIB0249|cit249","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.jns.2008.07.028","article-title":"Translation towards personalized medicine in multiple sclerosis","volume":"274","author":"Miller","year":"2008","journal-title":"Journal of the Neurological Sciences"},{"issue":"7","key":"10.1002\/14651858.CD013606.pub2-BIB0250|cit250","doi-asserted-by":"publisher","first-page":"e1000097","DOI":"10.1371\/journal.pmed.1000097","article-title":"Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement","volume":"6","author":"Moher","year":"2009","journal-title":"PLOS Medicine"},{"issue":"2","key":"10.1002\/14651858.CD013606.pub2-BIB0251|cit251","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1177\/1352458517751049","article-title":"ECTRIMS\/EAN Guideline on the pharmacological treatment of people with multiple sclerosis","volume":"24","author":"Montalban","year":"2018","journal-title":"Multiple Sclerosis Journal"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0252|cit252","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-642-35289-8","volume-title":"Neural networks: tricks of the trade","author":"Montavon","year":"2012"},{"issue":"10","key":"10.1002\/14651858.CD013606.pub2-BIB0253|cit253","doi-asserted-by":"publisher","first-page":"e1001744","DOI":"10.1371\/journal.pmed.1001744","article-title":"Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist","volume":"11","author":"Moons","year":"2014","journal-title":"PLOS Medicine"},{"issue":"1","key":"10.1002\/14651858.CD013606.pub2-BIB0254|cit254","doi-asserted-by":"publisher","first-page":"W1","DOI":"10.7326\/M18-1377","article-title":"PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration","volume":"170","author":"Moons","year":"2019","journal-title":"Annals of Internal Medicine"},{"issue":"4","key":"10.1002\/14651858.CD013606.pub2-BIB0255|cit255","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1002\/sim.2324","article-title":"Confidence intervals for an effect size measure based on the Mann\u2013Whitney statistic. Part 2: asymptotic methods and evaluation","volume":"25","author":"Newcombe","year":"2006","journal-title":"Statistics in Medicine"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0256|cit256","doi-asserted-by":"crossref","unstructured":"Niculescu-Mizil A Caruana R Predicting good probabilities with supervised learning Proceedings of the 22nd International Conference on Machine Learning 2005 625 32 10.1145\/1102351.1102430","DOI":"10.1145\/1102351.1102430"},{"issue":"10","key":"10.1002\/14651858.CD013606.pub2-BIB0257|cit257","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1016\/S1474-4422(19)30151-6","article-title":"Early highly effective versus escalation treatment approaches in relapsing multiple sclerosis","volume":"18","author":"Ontaneda","year":"2019","journal-title":"Lancet Neurology"},{"issue":"12","key":"10.1002\/14651858.CD013606.pub2-BIB0258|cit258","doi-asserted-by":"publisher","first-page":"1673","DOI":"10.1001\/archopht.1991.01080120057025","article-title":"The clinical profile of optic neuritis. Experience of the optic neuritis treatment trial","volume":"109","author":"Optic Neuritis Study Group","year":"1991","journal-title":"Archives of Ophthalmology"},{"issue":"1","key":"10.1002\/14651858.CD013606.pub2-BIB0259|cit259","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1186\/s13643-016-0384-4","article-title":"Rayyan - a web and mobile app for systematic reviews","volume":"5","author":"Ouzzani","year":"2016","journal-title":"Systematic Reviews"},{"issue":"3","key":"10.1002\/14651858.CD013606.pub2-BIB0260|cit260","doi-asserted-by":"publisher","first-page":"e1003583","DOI":"10.1371\/journal.pmed.1003583","article-title":"The PRISMA 2020 statement: an updated guideline for reporting systematic reviews","volume":"18","author":"Page","year":"2021","journal-title":"PLoS Medicine"},{"issue":"6460","key":"10.1002\/14651858.CD013606.pub2-BIB0261|cit261","doi-asserted-by":"publisher","first-page":"eaav7188","DOI":"10.1126\/science.aav7188","article-title":"Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility","volume":"365","author":"Patsopoulos","year":"2019","journal-title":"Science"},{"issue":"7","key":"10.1002\/14651858.CD013606.pub2-BIB0262|cit262","doi-asserted-by":"publisher","first-page":"e1001671","DOI":"10.1371\/journal.pmed.1001671","article-title":"Improving the transparency of prognosis research: the role of reporting, data sharing, registration, and protocols","volume":"11","author":"Peat","year":"2014","journal-title":"PLOS Medicine"},{"issue":"3","key":"10.1002\/14651858.CD013606.pub2-BIB0263|cit263","first-page":"61","article-title":"Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods","volume":"10","author":"Platt","year":"1999","journal-title":"Advances in Large Margin Classifiers"},{"issue":"6","key":"10.1002\/14651858.CD013606.pub2-BIB0264|cit264","doi-asserted-by":"publisher","first-page":"840","DOI":"10.1002\/ana.20703","article-title":"Diagnostic criteria for multiple sclerosis: 2005 revisions to the \"McDonald Criteria\"","volume":"58","author":"Polman","year":"2005","journal-title":"Annals of Neurology"},{"issue":"2","key":"10.1002\/14651858.CD013606.pub2-BIB0265|cit265","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1002\/ana.20703","article-title":"Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria","volume":"69","author":"Polman","year":"2011","journal-title":"Annals of Neurology"},{"issue":"3","key":"10.1002\/14651858.CD013606.pub2-BIB0266|cit266","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1002\/ana.410130302","article-title":"New diagnostic criteria for multiple sclerosis: guidelines for research protocols","volume":"13","author":"Poser","year":"1983","journal-title":"Annals of Neurology"},{"issue":"17","key":"10.1002\/14651858.CD013606.pub2-BIB0267|cit267","doi-asserted-by":"publisher","first-page":"789","DOI":"10.1212\/WNL.0000000000005345","article-title":"Comprehensive systematic review summary: disease-modifying therapies for adults with multiple sclerosis","volume":"90","author":"Rae-Grant","year":"2018","journal-title":"Neurology"},{"issue":"2","key":"10.1002\/14651858.CD013606.pub2-BIB0268|cit268","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1056\/NEJMra1401483","article-title":"Multiple sclerosis","volume":"378","author":"Reich","year":"2018","journal-title":"New England Journal of Medicine"},{"issue":"7","key":"10.1002\/14651858.CD013606.pub2-BIB0269|cit269","doi-asserted-by":"publisher","first-page":"1276","DOI":"10.1002\/sim.7992","article-title":"Minimum sample size for developing a multivariable prediction model: part II - binary and time-to-event outcomes","volume":"38","author":"Riley","year":"2019","journal-title":"Statistics in Medicine"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0270|cit270","doi-asserted-by":"publisher","first-page":"m441","DOI":"10.1136\/bmj.m441","article-title":"Calculating the sample size required for developing a clinical prediction model","volume":"368","author":"Riley","year":"2020","journal-title":"BMJ"},{"issue":"10","key":"10.1002\/14651858.CD013606.pub2-BIB0271|cit271","doi-asserted-by":"publisher","first-page":"2683","DOI":"10.1097\/ccm.0b013e3181ab85ec","article-title":"Baseline characteristics and statistical power in randomized controlled trials: selection, prognostic targeting, or covariate adjustment?","volume":"37","author":"Roozenbeek","year":"2009","journal-title":"Critical Care Medicine"},{"issue":"5","key":"10.1002\/14651858.CD013606.pub2-BIB0272|cit272","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1038\/s41582-019-0170-8","article-title":"Reaching an evidence-based prognosis for personalized treatment of multiple sclerosis","volume":"15","author":"Rotstein","year":"2019","journal-title":"Nature Reviews Neurology"},{"issue":"10","key":"10.1002\/14651858.CD013606.pub2-BIB0273|cit273","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1007\/BF00920623","article-title":"Prediction of outcome in multiple sclerosis based on multivariate models","volume":"241","author":"Runmarker","year":"1994","journal-title":"Journal of Neurology"},{"issue":"10","key":"10.1002\/14651858.CD013606.pub2-BIB0274|cit274","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1038\/nrneurol.2009.139","article-title":"Predicting responders to therapies for multiple sclerosis","volume":"5","author":"R\u00edo","year":"2009","journal-title":"Nature Reviews Neurology"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0275|cit275","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.jns.2015.12.043","article-title":"Short-term suboptimal response criteria for predicting long-term non-response to first-line disease modifying therapies in multiple sclerosis: a systematic review and meta-analysis","volume":"361","author":"R\u00edo","year":"2016","journal-title":"Journal of the Neurological Sciences"},{"issue":"10","key":"10.1002\/14651858.CD013606.pub2-BIB0276|cit276","doi-asserted-by":"publisher","first-page":"1174","DOI":"10.1177\/1352458511421106","article-title":"The major cause of multiple sclerosis is environmental: genetics has a minor role--no","volume":"17","author":"Sawcer","year":"2011","journal-title":"Multiple Sclerosis"},{"issue":"2","key":"10.1002\/14651858.CD013606.pub2-BIB0277|cit277","doi-asserted-by":"publisher","first-page":"122","DOI":"10.3390\/life11020122","article-title":"Machine learning use for prognostic purposes in multiple sclerosis","volume":"11","author":"Seccia","year":"2021","journal-title":"Life"},{"issue":"8","key":"10.1002\/14651858.CD013606.pub2-BIB0278|cit278","doi-asserted-by":"crossref","first-page":"e009972","DOI":"10.1136\/bmjopen-2015-009972","article-title":"Assessment of the extent of unpublished studies in prognostic factor research: a systematic review of p53 immunohistochemistry in bladder cancer as an example","volume":"6","author":"Sekula","year":"2016","journal-title":"BMJ Open"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0279|cit279","doi-asserted-by":"crossref","unstructured":"Simera I Altman DG Moher D Schulz KF Hoey J Guidelines for reporting health research: the EQUATOR Network's survey of guideline authors PLOS Medicine 2008 5 6 e139 10.1371\/journal.pmed.0050139","DOI":"10.1371\/journal.pmed.0050139"},{"issue":"1","key":"10.1002\/14651858.CD013606.pub2-BIB0280|cit280","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1186\/s12916-020-01766-9","article-title":"External validation of prognostic models predicting pre-eclampsia: individual participant data meta-analysis","volume":"18","author":"Snell","year":"2020","journal-title":"BMC Medicine"},{"issue":"5","key":"10.1002\/14651858.CD013606.pub2-BIB0281|cit281","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1177\/1352458512460605","article-title":"Scoring treatment response in patients with relapsing multiple sclerosis","volume":"19","author":"Sormani","year":"2013","journal-title":"Multiple Sclerosis Journal"},{"issue":"2","key":"10.1002\/14651858.CD013606.pub2-BIB0282|cit282","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1212\/WNL.0000000000002830","article-title":"Assessing response to interferon-\u03b2 in a multicenter dataset of patients with MS","volume":"87","author":"Sormani","year":"2016","journal-title":"Neurology"},{"issue":"3","key":"10.1002\/14651858.CD013606.pub2-BIB0283|cit283","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1177\/1352458516676899","article-title":"Prognostic factors versus markers of response to treatment versus surrogate endpoints: three different concepts","volume":"23","author":"Sormani","year":"2017","journal-title":"Multiple Sclerosis Journal"},{"issue":"2","key":"10.1002\/14651858.CD013606.pub2-BIB0284|cit284","doi-asserted-by":"publisher","first-page":"e1001381","DOI":"10.1371\/journal.pmed.1001381","article-title":"Prognosis research strategy (PROGRESS) 3: prognostic model research","volume":"10","author":"Steyerberg","year":"2013","journal-title":"PLOS Medicine"},{"issue":"5","key":"10.1002\/14651858.CD013606.pub2-BIB0285|cit285","doi-asserted-by":"crossref","first-page":"e38","DOI":"10.1093\/brain\/awy055","article-title":"Towards personalized therapy for multiple sclerosis: limitations of observational data","volume":"141","author":"Steyerberg","year":"2018","journal-title":"Brain"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0286|cit286","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-16399-0","volume-title":"Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating","author":"Steyerberg","year":"2019"},{"issue":"6","key":"10.1002\/14651858.CD013606.pub2-BIB0287|cit287","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1002\/1531-8249(200006)47:6&lt;831::AID-ANA21&gt;3.0.CO;2-H","article-title":"Diagnostic criteria for primary progressive multiple sclerosis: a position paper","volume":"47","author":"Thompson","year":"2000","journal-title":"Annals of Neurology"},{"issue":"10130","key":"10.1002\/14651858.CD013606.pub2-BIB0288|cit288","doi-asserted-by":"publisher","first-page":"1622","DOI":"10.1016\/S0140-6736(18)30481-1","article-title":"Multiple sclerosis","volume":"391","author":"Thompson","year":"2018","journal-title":"Lancet"},{"issue":"2","key":"10.1002\/14651858.CD013606.pub2-BIB0289|cit289","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/S1474-4422(17)30470-2","article-title":"Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria","volume":"17","author":"Thompson","year":"2018","journal-title":"Lancet Neurology"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0290|cit290","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1186\/1471-2288-14-137","article-title":"Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints","volume":"14","author":"Ploeg","year":"2014","journal-title":"BMC Medical Research Methodology"},{"issue":"3","key":"10.1002\/14651858.CD013606.pub2-BIB0291|cit291","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1007\/s40263-017-0412-5","article-title":"Outcome measures in clinical trials for multiple sclerosis","volume":"31","author":"Munster","year":"2017","journal-title":"CNS Drugs"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0292|cit292","unstructured":"van Smeden M Should a risk prediction model be developed?; 3 August 2018 https:\/\/twitter.com\/maartenvsmeden\/status\/1025315100796899328"},{"issue":"8","key":"10.1002\/14651858.CD013606.pub2-BIB0293|cit293","doi-asserted-by":"publisher","first-page":"573","DOI":"10.7326\/0003-4819-147-8-200710160-00010","article-title":"The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies","volume":"147","author":"Elm","year":"2007","journal-title":"Annals of Internal Medicine"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0294|cit294","doi-asserted-by":"publisher","first-page":"S90","DOI":"10.1016\/j.ejps.2017.05.026","article-title":"Model-based clinical dose optimization for phenobarbital in neonates: an illustration of the importance of data sharing and external validation","volume":"109","author":"V\u00f6ller","year":"2017","journal-title":"European Journal of Pharmaceutical Sciences"},{"issue":"14","key":"10.1002\/14651858.CD013606.pub2-BIB0295|cit295","doi-asserted-by":"publisher","first-page":"1816","DOI":"10.1177\/1352458520970841","article-title":"Rising prevalence of multiple sclerosis worldwide: Insights from the atlas of MS","volume":"26","author":"Walton","year":"2020","journal-title":"Multiple Sclerosis"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0296|cit296","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1007\/978-3-662-56784-5_2","volume-title":"Neurologische Pathophysiologie: Ursachen und Mechanismen neurologischer Erkrankungen","author":"Warnke","year":"2019"},{"issue":"1","key":"10.1002\/14651858.CD013606.pub2-BIB0297|cit297","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1093\/brain\/112.1.133","article-title":"The natural history of multiple sclerosis: a geographically based study. I. Clinical course and disability","volume":"112","author":"Weinshenker","year":"1989","journal-title":"Brain"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0298|cit298","doi-asserted-by":"publisher","first-page":"175628642110396","DOI":"10.1177\/17562864211039648","article-title":"Multiple Sclerosis Therapy Consensus Group (MSTCG): position statement on disease-modifying therapies for multiple sclerosis (white paper)","volume":"14","author":"Wiendl","year":"2021","journal-title":"Therapeutic Advances in Neurological Disorders"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0299|cit299","doi-asserted-by":"publisher","first-page":"i3518","DOI":"10.1136\/bmj.i3518","article-title":"Disease modifying therapies for relapsing multiple sclerosis","volume":"354","author":"Wingerchuk","year":"2016","journal-title":"BMJ"},{"issue":"1","key":"10.1002\/14651858.CD013606.pub2-BIB0300|cit300","doi-asserted-by":"publisher","first-page":"51","DOI":"10.7326\/M18-1376","article-title":"PROBAST: a tool to assess the risk of bias and applicability of prediction model studies","volume":"170","author":"Wolff","year":"2019","journal-title":"Annals of Internal Medicine"},{"issue":"3","key":"10.1002\/14651858.CD013606.pub2-BIB0301|cit301","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1111\/1471-0528.14170","article-title":"Key steps and common pitfalls in developing and validating risk models","volume":"124","author":"Wynants","year":"2017","journal-title":"BJOG: An International Journal of Obstetrics and Gynaecology"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0302|cit302","doi-asserted-by":"publisher","first-page":"m1328","DOI":"10.1136\/bmj.m1328","article-title":"Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal","volume":"369","author":"Wynants","year":"2020","journal-title":"BMJ"},{"key":"10.1002\/14651858.CD013606.pub2-BIB0303|cit303","unstructured":"Zadrozny B Elkan C Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers Proceedings of the Eighteenth International Conference on Machine Learning 2001 609 16"},{"issue":"5","key":"10.1002\/14651858.CD013606.pub2-BIB0304|cit304","doi-asserted-by":"publisher","DOI":"10.1002\/14651858.CD013606","article-title":"Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis","author":"Reeve","year":"2020","journal-title":"Cochrane Database of Systematic Reviews"}],"container-title":["Cochrane Database of Systematic Reviews"],"original-title":[],"language":"en","deposited":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T22:05:15Z","timestamp":1760565915000},"score":1,"resource":{"primary":{"URL":"http:\/\/doi.wiley.com\/10.1002\/14651858.CD013606.pub2"}},"subtitle":[],"editor":[{"name":"Cochrane Multiple Sclerosis and Rare Diseases of the CNS Group","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2023,9,8]]},"references-count":304,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,9]]}},"URL":"https:\/\/doi.org\/10.1002\/14651858.cd013606.pub2","relation":{},"ISSN":["1465-1858"],"issn-type":[{"value":"1465-1858","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,8]]}}}