Associations between media parenting practices and early adolescent screen use.

Background: To assess the prevalence of various media parenting practices and identify their associations with early adolescent screen time and problematic social media, video game, and mobile phone use.

Methods: Cross-sectional data from Year 3 of the Adolescent Brain Cognitive Development (ABCD) Study (2019-2022) that included 10,048 adolescents (12-13 years, 48.3% female, 45.

Social epidemiology of online dating in U.S. early adolescents.

Objective: To investigate the prevalence and sociodemographic associations of online dating in a demographically diverse U.S. national cohort of early adolescents.

Mental health symptoms are comparable in patients hospitalized with acute illness and patients hospitalized with injury.

Background: High rates of mental health symptoms such as depression, anxiety, and posttraumatic stress disorder (PTSD) have been found in patients hospitalized with traumatic injuries, but little is known about these problems in patients hospitalized with acute illnesses. A similarly high prevalence of mental health problems in patients hospitalized with acute illness would have significant public health implications because acute illness and injury are both common, and mental health problems of depression, anxiety, and PTSD are highly debilitating.

Methods And Findings: In patients admitted after emergency care for Acute Illness (N = 656) or Injury (N = 661) to three hospitals across the United States, symptoms of depression, anxiety, and posttraumatic stress were compared acutely (Acute Stress Disorder) and two months post-admission (PTSD).

Automated Computational Detection of Interstitial Fibrosis, Tubular Atrophy, and Glomerulosclerosis.

Background: Interstitial fibrosis, tubular atrophy (IFTA), and glomerulosclerosis are indicators of irrecoverable kidney injury. Modern machine learning (ML) tools have enabled robust, automated identification of image structures that can be comparable with analysis by human experts. ML algorithms were developed and tested for the ability to replicate the detection and quantification of IFTA and glomerulosclerosis that renal pathologists perform.

Automated En Masse Machine Learning Model Generation Shows Comparable Performance as Classic Regression Models for Predicting Delayed Graft Function in Renal Allografts.

Background: Several groups have previously developed logistic regression models for predicting delayed graft function (DGF). In this study, we used an automated machine learning (ML) modeling pipeline to generate and optimize DGF prediction models en masse.

Methods: Deceased donor renal transplants at our institution from 2010 to 2018 were included.