Human AI collaboration for unsupervised categorization of live surgical feedback.

Formative verbal feedback during live surgery is essential for adjusting trainee behavior and accelerating skill acquisition. Despite its importance, understanding optimal feedback is challenging due to the difficulty of capturing and categorizing feedback at scale. We propose a Human-AI Collaborative Refinement Process that uses unsupervised machine learning (Topic Modeling) with human refinement to discover feedback categories from surgical transcripts.

Identifying Individuals in the Prodromal Phase of Parkinson's Disease: A Prospective Cohort Study.

Objective: We prospectively evaluated how well combinations of signs and symptoms can identify individuals in the prodromal phase of Parkinson's disease (PD).

Methods: The study comprised 6,108 men who underwent repeated assessments of key prodromal features and were prospectively followed for the development of PD. Two composite measures of prodromal PD were evaluated: (i) the co-occurrence of constipation, probable rapid eye movement (REM) sleep behavior disorder (pRBD), and hyposmia, and (ii) the probability of prodromal PD based on the Movement Disorders Society (MDS) research criteria.

Effects of elastic therapeutic taping on reducing drooling in children with neurological disorders: a systematic review of randomized controlled trials.

Background & Aims: Effective treatment for anterior drooling in children with neurological disorders can lead to improved social interactions, reduced physical complications such as perioral infections, and enhanced quality of life for both patients and their parents. Elastic therapeutic taping (ETT) has emerged a novel intervention for drooling, but its evidence was limited. This study systematically reviewed the effectiveness of ETT on reducing anterior drooling in children with neurological disorders.

Molecular Dynamics Simulations of Structurally Nanoengineered Antimicrobial Peptide Polymers Interacting with Bacterial Cell Membranes.

Multidrug resistance (MDR) to conventional antibiotics is one of the most urgent global health threats, necessitating the development of effective and biocompatible antimicrobial agents that are less inclined to provoke resistance. Structurally nanoengineered antimicrobial peptide polymers (SNAPPs) are a novel and promising class of such alternatives. These star-shaped polymers are made of a dendritic core with multiple arms made of copeptides with varying amino acid sequences.

Cross-Slice Attention and Evidential Critical Loss for Uncertainty-Aware Prostate Cancer Detection.

Current deep learning-based models typically analyze medical images in either 2D or 3D albeit disregarding volumetric information or suffering sub-optimal performance due to the anisotropic resolution of MR data. Furthermore, providing an accurate uncertainty estimation is beneficial to clinicians, as it indicates how confident a model is about its prediction. We propose a novel 2.