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Objectives To investigate electric scooter (e-scooter)-associated radiological injury incidence and distribution of injuries. Methods Retrospective cross-sectional study of radiological examinations related to e-scooter injuries at a major trauma centre in a small university city. The hospital radiology information system was searched for terms related to e-scooters between January 1, 2015, and October 31, 2022. E-scooter use was confirmed by review of the patients' electronic medical records. Specific injuries were divided based on site of injury using the Injury Severity Scale categorized groups. Results A total of 568 radiological studies related to e-scooter injuries were identified on 340 distinct patients (56% male, with an average age of 28 years). Peak incidence of e-scooter-related injuries was seen in the summer months, after a local scooter sharing system was introduced in October 2020. A total of 149 patients had radiologically diagnosed injuries, with extremity injuries being most frequent (80%). Facial (8%), head/neck (8%), and thorax/abdomen (4%) were less common. Radial head fractures were the most common injury (n  = 27). Thirteen patients had multiple sites of injury, four of which had both upper limb and facial bone fractures described. Conclusions We report a significant increase in radiological investigations and injuries in the context of e-scooter injuries, particularly since the introduction of an e-scooter sharing scheme. This study informs radiologists on common locations of injuries when reporting studies of patients that have had e-scooter-related injuries. Advances in knowledge This is the first UK-based study providing a comprehensive radiological perspective of the impact of e-scooter use and associated distribution of injuries, adding important data for many cities that are currently undertaking review of their e-scooter sharing schemes. [ABSTRACT FROM AUTHOR]

Background: ChatGPT is a large language model that has performed well on professional examinations in the fields of medicine, law, and business. However, it is unclear how ChatGPT would perform on an examination assessing professionalism and situational judgement for doctors., Objective: We evaluated the performance of ChatGPT on the Situational Judgement Test (SJT): a national examination taken by all final-year medical students in the United Kingdom. This examination is designed to assess attributes such as communication, teamwork, patient safety, prioritization skills, professionalism, and ethics., Methods: All questions from the UK Foundation Programme Office's (UKFPO's) 2023 SJT practice examination were inputted into ChatGPT. For each question, ChatGPT's answers and rationales were recorded and assessed on the basis of the official UK Foundation Programme Office scoring template. Questions were categorized into domains of Good Medical Practice on the basis of the domains referenced in the rationales provided in the scoring sheet. Questions without clear domain links were screened by reviewers and assigned one or multiple domains. ChatGPT's overall performance, as well as its performance across the domains of Good Medical Practice, was evaluated., Results: Overall, ChatGPT performed well, scoring 76% on the SJT but scoring full marks on only a few questions (9%), which may reflect possible flaws in ChatGPT's situational judgement or inconsistencies in the reasoning across questions (or both) in the examination itself. ChatGPT demonstrated consistent performance across the 4 outlined domains in Good Medical Practice for doctors., Conclusions: Further research is needed to understand the potential applications of large language models, such as ChatGPT, in medical education for standardizing questions and providing consistent rationales for examinations assessing professionalism and ethics., (©Robin J Borchert, Charlotte R Hickman, Jack Pepys, Timothy J Sadler. Originally published in JMIR Medical Education (https://mededu.jmir.org), 07.08.2023.)