Volumetric Humeral Canal Fill Ratio Effects Primary Stability and Cortical Bone Loading in Short and Standard Stem Reverse Shoulder Arthroplasty: A Biomechanical and Computational Study.

Objective: This study evaluated the effect of three-dimensional (3D) volumetric humeral canal fill ratios (VFR) of reverse shoulder arthroplasty (RSA) short and standard stems on biomechanical stability and bone deformations in the proximal humerus.

Methods: Forty cadaveric shoulder specimens were analyzed in a clinical computed tomography (CT) scanner allowing for segmentation of the humeral canal to calculate volumetric measures which were verified postoperatively with plain radiographs. Virtual implant positioning allowed for group assignment (VFR < 0.

Therapeutic Arthroscopy for Noninfectious Stiffness and Anterior Shoulder Pain After Reverse Shoulder Arthroplasty Leads to Clinical Improvement in Most Patients with a Low Complication Rate.

Hypothesis And Background: Reverse shoulder arthroplasty (RSA) exhibits favorable outcomes in managing rotator cuff arthropathy, primary glenohumeral arthritis, and complex proximal humeral fractures. Despite its success and reliability, certain patients experience persistent pain and stiffness. The clinical utility of therapeutic arthroscopy in RSA patients remains an area for investigation.

Machine Learning Classification of Pediatric Health Status Based on Cardiorespiratory Signals with Causal and Information Domain Features Applied-An Exploratory Study.

This study aimed to evaluate the accuracy of machine learning (ML) techniques in classifying pediatric individuals-cardiological patients, healthy participants, and athletes-based on cardiorespiratory features from short-term static measurements. It also examined the impact of cardiorespiratory coupling (CRC)-related features (from causal and information domains) on the modeling accuracy to identify a preferred cardiorespiratory feature set that could be further explored for specialized tasks, such as monitoring training progress or diagnosing health conditions. We utilized six self-prepared datasets that comprised various subsets of cardiorespiratory parameters and applied several ML algorithms to classify subjects into three distinct groups.