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.