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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Rethinking cognitive assessment in mild intellectual disability: evidence-based recommendations for a person-centered
1Laboratory of Psychological and Educational Tests, Gdansk, Poland.
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This article proposes a multidimensional, person-centered framework for diagnosing mild intellectual disability (MID), challenging the traditional reliance on fixed IQ thresholds as the primary diagnostic criterion. Anchored in the Cattell-Horn-Carroll (CHC) theory of intelligence, the framework advocates for an integrative assessment approach that combines broad (Stratum II) and narrow (Stratum I) cognitive abilities with evaluations of adaptive behavior, clinical observation, and informant reports. This comprehensive model reflects the complexity and heterogeneity of MID, emphasizing that intellectual functioning cannot be accurately captured by a single numerical score. The framework underscores the importance of using ecologically valid and culturally sensitive tools, particularly for assessing functionally relevant domains such as memory, language, and literacy. It highlights the need for clinical judgment to be informed by structured tools that map individual cognitive profiles and guide intervention planning in alignment with real-world demands. Central to this approach is the recognition of intra-group variability and the presence of distinct cognitive subprofiles, which call for tailored interventions based on individual strengths and needs rather than rigid diagnostic categories. Drawing on recent empirical studies, the article provides evidence supporting the existence of these cognitive subtypes within MID populations and advocates for individualized, strength-based assessment and support strategies. It calls for a longitudinal, multi-method, and context-sensitive diagnostic process grounded in both theoretical models and clinical expertise. Ultimately, the proposed paradigm promotes diagnostic accuracy, ethical integrity, and equity by shifting from reductionist classification toward a nuanced understanding of cognitive functioning and human potential.
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