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The study sample was stratified by sex and then split into training (80%) and testing (20%) datasets, followed by outlier removal, and mean-centring. Normative models were generated through eight different algorithms and compared in terms of accuracy and computational efficiency. Explanatory variables were added to identify the appropriate combination for optimal model performance.

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