Introduction: Overcrowding in emergency departments (ED) is a major public health issue, leading to increased workload and exhaustion for the teams, resulting poor outcomes. It seems interesting to be able to predict the admissions of patients in the ED.
Aim: The main objective of this study was to build and test a prediction tool for ED admissions using artificial intelligence.
Background: Loneliness and isolation impact health detrimentally but are understudied in Parkinson's disease (PD). Outcome measurement properties for social connection remain unexplored in PD.
Objective: To evaluate the measurement properties of six social connection outcomes in PD.
Purpose: Intravitreal injections with anti-vascular endothelial growth factor (VEGF) drugs can slow progression in neovascular age-related macular degeneration (nAMD). Best spectacle-corrected visual acuity (BSCVA) and/or central retinal thickness (CRT) are common barometers of efficacy of this treatment. However, BSCVA does not accurately measure reading ability, which is often severely impacted by nAMD.
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