Feasibility of ultraportable US in detecting clinically concerning recurrent pneumothorax in patients with chest trauma.

Background: Bedside thoracic ultrasound (US) offers numerous advantages over chest X-ray (CXR) for identification of recurrent pneumothoraces (PTX) after tube thoracostomy (TT) removal. Technologic advancements have led to the development of hand-held devices capable of producing high-quality images termed ultra-portable US (UPUS). We hypothesized that UPUS would be as successful as CXR in detecting post-TT removal PTX and would be preferred by patients.

Artificial Intelligence in Health Professions Education assessment: AMEE Guide No. 178.

Health Professions Education (HPE) assessment is being increasingly impacted by Artificial Intelligence (AI), and institutions, educators, and learners are grappling with AI's ever-evolving complexities, dangers, and potential. This AMEE Guide aims to assist all HPE stakeholders by helping them navigate the assessment uncertainty before them. Although the impetus is AI, the Guide grounds its path in pedagogical theory, considers the range of human responses, and then deals with assessment types, challenges, AI roles as tutor and learner, and required competencies.

Predictors of End-of-Life Care in Nonelderly Adults With Severe Traumatic Brain Injury.

Introduction: Older age is a well-established risk factor for withdrawal of life-sustaining therapy (WDLST) and discharge to hospice (DH) in traumatic brain injury (TBI). However, a paucity of data exists in identifying factors associated with end-of-life (EoL) care in younger patients with TBI. We sought to identify hospital and patient factors associated with EoL care and timing of EoL care in younger adults with severe TBI.

Validation of the Blended Learning Usability Evaluation–Questionnaire (BLUE-Q) through an innovative Bayesian questionnaire validation approach.

Purpose: The primary aim of this study is to validate the Blended Learning Usability Evaluation–Questionnaire (BLUE-Q) for use in the field of health professions education through a Bayesian approach. As Bayesian questionnaire validation remains elusive, a secondary aim of this article is to serve as a simplified tutorial for engaging in such validation practices in health professions education.

Methods: A total of 10 health education-based experts in blended learning were recruited to participate in a 30-minute interviewer-administered survey.