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The effect of digital parental awareness and somatic symptoms on problematic media use in children with attention-deficit/hyperactivity disorder.

J Pediatr Nurs

December 2024

Dicle University, Faculty of Medicine, Department of Child and Adolescent Psychiatry, Diyarbakır, Turkey. Electronic address:

Background: Children diagnosed with Attention-deficit/hyperactivity disorder (ADHD) have greater difficulty in regulating their own media usage and are inclined to spend more time engaging in video games compared to neurotypical children. This study aimed to investigate the effects of ADHD severity, digital parenting awareness, and accompanying psychiatric symptoms on children's problematic media (PMU) use in children with ADHD.

Methods: The study included 95 ADHD patients aged between 6 and 11 years and 90 age-and gender-matched healthy controls.

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Background/aim: This study evaluated the diagnostic accuracy (DA) for colorectal adenomas (CRA), screened by fecal immunochemical test (FIT), using five artificial intelligence (AI) models: logistic regression (LR), support vector machine (SVM), neural network (NN), random forest (RF), and gradient boosting machine (GBM). These models were tested together with clinical features categorized as low-risk (lowR) and high-risk (highR).

Patients And Methods: The colorectal neoplasia (CRN) screening cohort of 5,090 patients included 222 CRA patients and 264 non-CRA patients.

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With the global population projected to reach 9.7 billion by 2050, pressure on global natural resources will increase by 50-90%, exceeding planetary boundaries. Industry 4.

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Child anthropometric deficits remain a major public health problem in Sub-Saharan Africa (SSA) and are a key target of the UN Sustainable Development Goals (SDGs). The SDGs recommend disaggregation of health indicators by ethnic group. However, few studies have assessed how ethnicity is associated with anthropometric deficits across SSA.

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Testing for a difference in means of a single feature after clustering.

Biostatistics

December 2024

Department of Statistics, University of British Columbia, 3182 Earth Sciences Building, 2207 Main Mall, Vancouver, BC V6T 1Z4, Canada.

For many applications, it is critical to interpret and validate groups of observations obtained via clustering. A common interpretation and validation approach involves testing differences in feature means between observations in two estimated clusters. In this setting, classical hypothesis tests lead to an inflated Type I error rate.

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