Towards Correct and Safe Diagnosis of Specific Learning Disorder in Preschool Age. The perspective of Early Multi-collector Diagnostic Approaches. A Pilot Study
Victoria Zakopoulou1*, Katerina D Tzimourta2,5, Georgios Ntritsos3,4, Alexandros T Tzallas4, Markos G Tsipouras5, Loukas G Astrakas2, Pavlos Christodoulides1, Ioannis Paliokas6, Vassilis Zakopoulos7 and Nikolaos Giannakeas4
1Department of Speech and Language Therapy, Laboratory of New Approaches in Communication Disorders, University of Ioannina, Ioannina, Epirus, Greece
2Medical Physics Laboratory, School of Medicine, University of Ioannina, Ioannina, Greece
3Department of Hygiene and Epidemiology, School of Medicine, University of Ioannina, Ioannina, Greece
4Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Ioannina, Greece
550100, Kozani, Greece
6Information Technologies Institute, Centre of Research and Technology – Hellas 6th km Harilaou - Thermi, Thessaloniki, Greece
7Department of Accounting and Finance, School of Administrative, Economics, and Social Sciences, University of West Attica, Athens, Greece
*Corresponding Author: Victoria Zakopoulou, Department of Speech and Language Therapy, Laboratory of New Approaches in Communication Disorders, University of Ioannina, Ioannina, Epirus, Greece.
Received:
April 08, 2021; Published: May 17, 2021
Abstract
Background: The wide range of terminology, multiple diagnostic criteria, and multifarious basis of Specific Learning Disorder (SLD) constitutes a new reality in the complex entity of SLD. In this manuscript, we address this issue and present the findings of a pilot study concerning the early diagnosis of SLD, while strongly emphasizing the necessity of integrating and testing a vast range of data from multiple domains and skills to achieve correct and safe early diagnosis of SLD.
Materials and Methods: For this purpose, statistical techniques were implemented in a well-structured methodological approach, as follows: (a) a cluster of adequate diagnostic procedures to determine the early extent of specified difficulties, (b) targeted data clustering techniques to identify clusters in the data, and (c) the Use Case method for the configuration of individualized diagnostic profiles.
Results: Through a data analysis schema, several variables were reported as significant, clustering the participants according to their strengths and weaknesses, while strong interactions between specific factors were highlighted in the background of SLD.
Conclusion: The findings of the study enhance the core argument of this pilot study that an “ever-expanding model” should be considered as the most reliable source for a comprehensive early diagnosis of SLD.
Keywords: Specific Learning Disorder; Early Diagnosis; Multifactorial Approach; Early Intervention; Clustering
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