The use of an automatic remote weight management system to track treatment response, identified drugs supply shortage and its consequences: A pilot study.

Objective: The objective of this pilot study is to evaluate the feasibility of using an automatic weight management system to follow patients' response to weight reduction medications and to identify early deviations from weight trajectories.

Methods: The pilot study involved 11 participants using Semaglutide for weight management, monitored over a 12-month period. A cloud-based, Wi-Fi-enabled remote weight management system collected and analyzed daily weight data from smart scales.

Primary and revision artificial urinary sphincter for stress urinary incontinence post-radical prostatectomy: a surgery with high rewards but high risks?

Objectives: To evaluate the efficacy of artificial urinary sphincter (AUS) implantation in men with stress urinary incontinence post-radical prostatectomy and the complication burden with a focus on identifying potential risk factors for reoperation as well as determining the fate of revision surgeries.

Methods: Retrospective analysis of consecutive patients undergoing primary AUS (pAUS) and revision AUS (rAUS) implantation at a tertiary centre. Logistic regression was employed to identify risk factors for reoperation associated with non-mechanical failures.

Clinician Experiences With Integrative Group Medical Visits for Chronic Pain.

Background: The well-being of healthcare teams is an important consideration when seeking to improve patient experience and quality of care. Prior studies have found that changes to working conditions are most effective in improving clinician well-being. Integrative Group Medical Visits (IGMVs) modify working conditions in ways that have potential to improve clinician experience.

Robustly learning the Hamiltonian dynamics of a superconducting quantum processor.

Precise means of characterizing analog quantum simulators are key to developing quantum simulators capable of beyond-classical computations. Here, we precisely estimate the free Hamiltonian parameters of a superconducting-qubit analog quantum simulator from measured time-series data on up to 14 qubits. To achieve this, we develop a scalable Hamiltonian learning algorithm that is robust against state-preparation and measurement (SPAM) errors and yields tomographic information about those SPAM errors.