{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T05:13:40Z","timestamp":1764220420677,"version":"3.46.0"},"reference-count":34,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T00:00:00Z","timestamp":1757894400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100021856","name":"Ministero dell'Universit\u00e0 e della Ricerca","doi-asserted-by":"publisher","award":["2022XRHT8R"],"award-info":[{"award-number":["2022XRHT8R"]}],"id":[{"id":"10.13039\/501100021856","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Dyslexia"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>The multi\u2010deficit framework employed to identify dyslexia requires statistical learning\u2010based models to account for the complex interplay of cognitive skills. Traditional methods often rely on simplistic statistical techniques, which may fail to capture the heterogeneity inherent in dyslexia. This study introduces a model\u2010based clustering framework, employing finite mixtures of contaminated Gaussian distributions, to better understand and classify dyslexia. Using data from a cohort of 122 children in Poland, including 51 diagnosed with dyslexia, we explore the effectiveness of this method in distinguishing between dyslexic and control groups. Our approach integrates variable selection techniques to identify clinically relevant cognitive skills while addressing issues of outliers and redundant variables. Results demonstrate the superiority of multivariate finite mixture models, achieving high accuracy in clustering and revealing the importance of specific variables such as Reading, Phonology, and Rapid Automatized Naming. This study emphasises the value of the multiple\u2010deficit model and robust statistical techniques in advancing the diagnosis and understanding of dyslexia.<\/jats:p>","DOI":"10.1002\/dys.70013","type":"journal-article","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T03:20:25Z","timestamp":1757992825000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Statistical Learning\u2010Based Clustering Model With Features Selection to Identify Dyslexia in School\u2010Aged Children"],"prefix":"10.1002","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4017-3769","authenticated-orcid":false,"given":"Michele","family":"Maiella","sequence":"first","affiliation":[{"name":"Department of Behavioural and Clinical Neurology Santa Lucia Foundation IRCCS  Rome Italy"},{"name":"Department of Neuroscience and Rehabilitation University of Ferrara and Center for Translational Neurophysiology of Speech and Communication (CTNSC), Italian Institute of Technology (IIT)  Ferrara Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martina","family":"Benedetti","sequence":"additional","affiliation":[{"name":"Department GEPLI LUMSA University  Rome Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6075-3659","authenticated-orcid":false,"given":"Pierfrancesco","family":"Alaimo Di Loro","sequence":"additional","affiliation":[{"name":"Department GEPLI LUMSA University  Rome Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8377-9950","authenticated-orcid":false,"given":"Antonello","family":"Maruotti","sequence":"additional","affiliation":[{"name":"Department GEPLI LUMSA University  Rome Italy"},{"name":"Departiment of Public Health and Epidemiology Khalifa University  Abu Dhabi 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