Robin Jacob Borchert

To investigate electric scooter (e-scooter)-associated radiological injury incidence and distribution of injuries. 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. 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. 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. 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.

Introduction: Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia.Methods: We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases.Results: A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort.Discussion: The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice.Highlights: There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias

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.

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.

Background and purpose Cone-beam CT angiography (CB-CTA) provides a three-dimensional spatial resolution which is, so far, unmatched in clinical practice compared with other conventional techniques such as two-dimensional digital subtracted angiography. We aimed to assess the distribution of symptomatic cerebral vasospasm following aneurysmal subarachnoid hemorrhage (aSAH) using CB-CTA. Methods 30 consecutive patients with aSAH undergoing vasospasm percutaneous balloon angioplasty (PBA) were recruited and underwent CB-CTA in this single-center prospective cohort series. Intracranial arteries were systematically analyzed by two independent observers from the large trunks to the distal cortical branches and perforators using a high-resolution reconstruction protocol. Intermediate and severe cerebral vasospasm was defined as 30–50% and >50% narrowing in the diameter of the vessel, respectively. Results 35 arterial cervical artery territories were analyzed, of which 80% were associated with clinical or radiological signs of delayed cerebral ischemia. The median spatial resolution was 150 µm (range 100–250 µm). Intermediate or severe vasospasm was observed in the proximal (86%, 95% CI 74% to 97%), middle (89%, 95% CI 78% to 99%), and distal (60%, 95% CI 44% to 76%) segments of the large trunks, as well as the cortical branches (11%, 95% CI 1% to 22%). No vasospasm was observed in basal ganglia or cortical perforators, or in arteries smaller than 900 µm. Vasospasm was more severe in middle or distal segments compared with proximal segments in 43% (95% CI 26% to 59%) of cases. Conclusions Our study demonstrated that symptomatic cerebral vasospasm following aSAH did not involve arteries smaller than 900 µm, and frequently predominated in middle or distal segments. These results offer new insights into the potential management options for vasospasm using PBA.

The use of antiplatelets is widespread in clinical practice. However, for neurointerventional procedures, protocols for antiplatelet use are scarce and practice varies between individuals and institutions. This is further complicated by the quantity of antiplatelet agents which differ in route of administration, dosage, onset of action, efficacy and ischemic and hemorrhagic complications. Clarifying the individual characteristics for each antiplatelet agent, and their associated risks, will increasingly become relevant as the practice of mechanical thrombectomy, stenting, coiling and flow diversion procedures grows. The aim of this review is to summarize the existing literature for the use of P2Y12 inhibitors in neurointerventional procedures, examine the quality of the evidence, and highlight areas in need of further research.