AI-assisted exposure-response data analysis: Quantifying heterogeneous causal effects of exposures on survival times.

AI-assisted data analysis can help risk analysts better understand exposure-response relationships by making it relatively easy to apply advanced statistical and machine learning methods, check their assumptions, and interpret their results. This paper demonstrates the potential of large language models (LLMs), such as ChatGPT, to facilitate statistical analyses, including survival data analyses, for health risk assessments. Through AI-guided analyses using relatively recent and advanced methods such as Individual Conditional Expectation (ICE) plots using Random Survival Forests and Heterogeneous Treatment Effects (HTEs) estimated using Causal Survival Forests, population-level exposure-response functions can be disaggregated into individual-level exposure-response functions.

Evaluation of an interprofessional collaborative practice training module for the management of children with autism spectrum disorder.

Background: Protocols instituted for behavioral treatment and skills training programs for the management of autism spectrum disorder (ASD) suffer from lack of collaborative approaches. The tenets of interprofessional collaborative practice (IPCP) focus on preparing a panel of health care professionals (HCPs) from different professions who can work together to enable the common goal of ensuring that children with ASD can participate in society. This study was designed to pilot this approach through an IPCP training module on ASD for care providers from multiple professions.

Marginal health care expenditures and health-related quality of life burden in patients with osteoporosis in the United States.

Background: Osteoporosis, marked by reduced bone density, significantly impacts quality of life. Recent estimates on its economic and humanistic burden in the United States are scarce.

Objective: To evaluate the marginal burden of osteoporosis on total all-cause health care costs and health-related quality of life (HRQoL) in the United States.

New Diagnoses of Juvenile Idiopathic Arthritis Early in the COVID-19 Pandemic: Analysis of a Large United States Commercial Insurance Database.

Background/objective: Little is known about the rates of rheumatic disease diagnosis among children during the COVID-19 pandemic. We examined the impact of the pandemic on the diagnosis of juvenile idiopathic arthritis (JIA) in the United States.

Methods: We performed a historical cohort study using US commercial insurance data (2016-2021) to identify children aged <18 years without prior JIA diagnosis or treatment in the prior ≥12 months.

High Diagnostic Accuracy of Arterial Phase CT in Differentiating Pheochromocytoma in Good/Poor Washout Adrenal Masses.

Introduction: Differentiating pheochromocytomas from other adrenal masses based on computed tomography (CT) characteristics remains challenging, particularly in lipid-poor lesions with variable washout patterns. This study evaluated CT features for distinguishing pheochromocytomas in good and poor washout subcohorts.

Methods: We prospectively analyzed 72 patients with unilateral lipid-poor adrenal masses.