Utilizing objective performance indicators to assess resident autonomy during robotic cholecystectomy.

Background: The current evaluation of surgical resident operative autonomy consists primarily of self-report and is prone to bias. Objective performance indicators (OPIs) generated from the da Vinci Surgical System capture objective intraoperative data providing an opportunity to evaluate the intraoperative resident experience more accurately. This study investigates the ability of OPIs to describe resident autonomy during robotic cholecystectomy.

Adapting self-measured blood pressure monitoring to reduce health disparities (ASPIRE): a pilot hybrid effectiveness‑implementation study protocol.

Background: Hypertension is the leading risk factor for cardiovascular disease (CVD). Despite advances in blood pressure management, significant racial and ethnic disparities persist, resulting in higher risks of stroke, heart disease, and mortality among non-White populations. Self-measured blood pressure (SMBP) monitoring, also known as home blood pressure monitoring, has shown promise in improving blood pressure control, especially when combined with feedback from healthcare providers.

Increased Afferent Nerve Firing Is Correlated With the Detection of Bladder Wall Micromotion in a Perfused Ex-Vivo Porcine Model.

Introduction And Objective: Observable autonomous rhythmic changes in intravesical pressure, termed bladder wall micromotion, is a phenomenon that has been linked to urinary urgency, the key symptom in overactive bladder (OAB). However, the mechanism through which micromotion drives urinary urgency is poorly understood. In addition, micromotion is inherently difficult to study in human urodynamics due to challenges distinguishing it from normal cyclic physiologic processes such as pulse rate, breathing, rectal contractions, and ureteral jetting.

Synergistic learning with multi-task DeepONet for efficient PDE problem solving.

Multi-task learning (MTL) is an inductive transfer mechanism designed to leverage useful information from multiple tasks to improve generalization performance compared to single-task learning. It has been extensively explored in traditional machine learning to address issues such as data sparsity and overfitting in neural networks. In this work, we apply MTL to problems in science and engineering governed by partial differential equations (PDEs).