Introduction: Prevalence estimates of opioid use disorder (OUD) at local levels are critical for public health planning and surveillance, yet largely unavailable across the US especially at the local county level.
Methods: We used a Bayesian evidence synthesis approach to estimate the prevalence of OUD for 57 counties across New York State for 2017-2019 and compare rates of OUD across counties as well as assess the extent of undiagnosed OUD. We developed a generative model to assess conditional probabilistic relations between different subgroups of the OUD population defined by diagnosis, treatment, and overdose fatality.
Background: The increasing use of social media to share lived and living experiences of substance use presents a unique opportunity to obtain information on side effects, use patterns, and opinions on novel psychoactive substances. However, due to the large volume of data, obtaining useful insights through natural language processing technologies such as large language models is challenging.
Objective: This paper aims to develop a retrieval-augmented generation (RAG) architecture for medical question answering pertaining to clinicians' queries on emerging issues associated with health-related topics, using user-generated medical information on social media.
Mol Ther Methods Clin Dev
December 2024
Background And Objective: Apalutamide (APA) is a treatment for metastatic castration-sensitive prostate cancer (mCSPC). In the ARON-3 study we investigated real-world experiences with APA treatment for mCSPC.
Methods: We retrospectively assessed real-world clinical outcomes for patients with mCSPC treated with APA in the ARON-3 study.