Predicting remission following CBT for childhood anxiety disorders: a machine learning approach.

Background: The identification of predictors of treatment response is crucial for improving treatment outcome for children with anxiety disorders. Machine learning methods provide opportunities to identify combinations of factors that contribute to risk prediction models.

Methods: A machine learning approach was applied to predict anxiety disorder remission in a large sample of 2114 anxious youth (5-18 years).

A Comparative Analysis of the Electrical Properties of Silicone Rubber Composites with Graphene and Unwashed Magnetite.

Three elastomer samples were prepared using GS530SP01K1 silicone rubber (ProChima). The samples included pure silicone rubber (SR), a silicone rubber-graphene composite (SR-GR), and a silicone rubber-magnetite composite (SR-FeO). The magnetite was synthesized via chemical precipitation but was not washed to remove residual ions.

Infectious morbidity and white blood cell count associated with grade repetition and school absenteeism.

Aim: Infections can impair cognitive development, but their role on adverse childhood educational outcomes is unknown. We examined the associations of infectious morbidity and inflammatory biomarkers with grade repetition and school absenteeism.

Methods: We followed 2762 Colombian children aged 5-12 years for a school year.

Understanding Impostorism in the Pediatric Surgeon.

Objectives: Impostor phenomenon (IP) is defined as feeling inadequacy, self-doubt, and the tendency to attribute achievement to external causes. We sought to examine IP rates among pediatric surgeons and to identify IP-associated factors, based on the hypothesis that pediatric surgeons experience imposterism, especially in the first few years of practice.

Design: Anonymous survey, including the validated Clance IP Scale (CIPS), distributed to pediatric surgeons.