Background: In healthcare systems in general, access to diabetic retinopathy (DR) screening is limited. Artificial intelligence has the potential to increase care delivery. Therefore, we trained and evaluated the diagnostic accuracy of a machine learning algorithm for automated detection of DR.
View Article and Find Full Text PDFBackground And Objectives: Individuals with obesity often face obesity bias, which may influence the delivery of appropriate medical care. Our aim is to evaluate the adequacy of therapeutic decisions regarding the pharmacological treatment for hypertension in patients with diabetes, both with and without obesity.
Methods: This is a multicentric cross-sectional study of patients with type 2 diabetes and arterial hypertension who received outpatient care in Southern Brazil.
Background: Patient navigation helps with better adherence to treatment, as well as better knowledge about diabetes and greater interest in performing, monitoring, and seeking health care. Therefore, this study aims to evaluate the effect of patient navigation on glycemic control, disease knowledge, adherence to self-care in people with type 1 diabetes mellitus.
Methods: This is an intervention study using a single group pre-test post-test design, carried out in a tertiary public teaching hospital in Southern Brazil.
Objective: To evaluate the association between knowledge about the disease, adherence to self-care, and glycemic control in people diagnosed with type 1 diabetes mellitus.
Subjects And Methods: A cross-sectional study of patients aged over 18 years diagnosed with type 1 diabetes mellitus, treated at an outpatient clinic of a Brazilian university hospital. Participants with other types of diabetes, cognitive impairment, pregnancy, and outpatient discharge were excluded.