Neovascular Glaucoma as the First Symptom of Bilateral Occlusive Retinal Vasculitis in a 4-Year-Old Girl: A Case Report.

Neovascular glaucoma is a rare and serious condition typically associated with advanced ocular or systemic vascular diseases such as central retinal vein occlusion or diabetic retinopathy. This report describes a unique case of neovascular glaucoma presenting for the first time as an initial symptom of bilateral occlusive retinal vasculitis (ORV) in a generally healthy 4-year-old girl. The patient presented with symptoms of pain and redness in the left eye, accompanied by high intraocular pressure.

Early Restrictive vs Liberal Oxygen for Trauma Patients: The TRAUMOX2 Randomized Clinical Trial.

Importance: Early administration of supplemental oxygen for all severely injured trauma patients is recommended, but liberal oxygen treatment has been associated with increased risk of death and respiratory complications.

Objective: To determine whether an early 8-hour restrictive oxygen strategy compared with a liberal oxygen strategy in adult trauma patients would reduce death and/or major respiratory complications.

Design, Setting, And Participants: This randomized controlled trial enrolled adult trauma patients transferred directly to hospitals, triggering a full trauma team activation with an anticipated hospital stay of a minimum of 24 hours from December 7, 2021, to September 12, 2023.

Eye tracking during a simulated start of shift safety check: An observational analysis of gaze behavior of critical care nurses.

Background: The handover and associated shift start checks by nurses of critical care patients are complex and prone to errors. However, which aspects lead to errors remains unknown. Fewer errors might occur in a structured approach.

Prior knowledge-guided vision-transformer-based unsupervised domain adaptation for intubation prediction in lung disease at one week.

Data-driven approaches have achieved great success in various medical image analysis tasks. However, fully-supervised data-driven approaches require unprecedentedly large amounts of labeled data and often suffer from poor generalization to unseen new data due to domain shifts. Various unsupervised domain adaptation (UDA) methods have been actively explored to solve these problems.