Characteristics of refractory disease and persistent symptoms in inflammatory arthritis: Qualitative framework analysis of interviews with patients and health care professionals.

Objectives: This study aims to explore patients' and clinicians' understanding and experiences of refractory disease (RD) and persistent physical and emotional symptoms (PPES) in patients with inflammatory arthritis (IA), namely rheumatoid arthritis or polyarticular juvenile idiopathic arthritis from their perspectives through interviews and/or focus groups.

Design: A qualitative study was conducted, following a pragmatic epistemology approach with framework analysis employed.

Methods: Semi-structured interviews or focus groups with IA patients (n = 25) and multi-disciplinary rheumatology HCPs (n = 32) were conducted at one time point to obtain participants respective understanding and experiences of managing RD/PPES, and its impact on the patient-professional relationship.

Dilute Povidone-Iodine Irrigation: The Science of Molecular Iodine (I2) Kinetics and Its Antimicrobial Activity.

Dilute povidone-iodine (polyvinylpyrrolidone iodine [PVP-I]) irrigation in spine surgery and total joint arthroplasty has seen a rapid and substantial increase in its use during the past decade. Yet, most surgeons do not know the chemistry and biochemistry that explain its efficacy in preventing infections. PVP-I forms a complex with molecular iodine (I2), facilitating the delivery of I2 to the membrane of the infectious organism.

Can Artificial Intelligence Deceive Residency Committees? A Randomized Multicenter Analysis of Letters of Recommendation.

Introduction: The introduction of generative artificial intelligence (AI) may have a profound effect on residency applications. In this study, we explore the abilities of AI-generated letters of recommendation (LORs) by evaluating the accuracy of orthopaedic surgery residency selection committee members to identify LORs written by human or AI authors.

Methods: In a multicenter, single-blind trial, a total of 45 LORs (15 human, 15 ChatGPT, and 15 Google BARD) were curated.

Can Artificial Intelligence Fool Residency Selection Committees? Analysis of Personal Statements by Real Applicants and Generative AI, a Randomized, Single-Blind Multicenter Study.

Introduction: The potential capabilities of generative artificial intelligence (AI) tools have been relatively unexplored, particularly in the realm of creating personalized statements for medical students applying to residencies. This study aimed to investigate the ability of generative AI, specifically ChatGPT and Google BARD, to generate personal statements and assess whether faculty on residency selection committees could (1) evaluate differences between real and AI statements and (2) determine differences based on 13 defined and specific metrics of a personal statement.

Methods: Fifteen real personal statements were used to generate 15 unique and distinct personal statements from ChatGPT and BARD each, resulting in a total of 45 statements.