The assessment of attention deficit/hyperactivity disorder (ADHD), conduct disorder (CD), and oppositional defiant disorder (ODD) has drawn much interest because of their prevalence in children and adolescents, comorbid problems, and adverse correlates and outcomes. Evaluation of these disorders via structured diagnostic interviews can be time-consuming and researchers have developed shorter inventories to screen for ADHD, CD, and ODD diagnoses. An example of a relatively short screening tool are the DISC Predictive Scales (DPS). To ensure that group comparisons with DPS scores are valid, measurement invariance across sociodemographic groups (e.g., gender, ethnic/racial groups) must be established. This is addressed in this paper using a large dataset of 4,491 children and confirmatory factor analysis and multiple indicators multiple causes modeling.  Our findings for the parent-version of the DPS imply that practitioners and researchers can compare the scale scores of the ADHD, CD, and ODD symptoms across gender and African-American, Latino, and White children with minimal bias. However, caution should be exercised when comparing these groups on individual items of the DPS.

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The Oxford Classification for IgA nephropathy is the most successful example of an evidence-based nephropathology classification system. The aim of our study was to replicate the glomerular components of Oxford scoring with an end-to-end deep learning pipeline that involves automatic glomerular segmentation followed by classification for mesangial hypercellularity (M), endocapillary hypercellularity (E), segmental sclerosis (S) and active crescents (C).