Harnessing ChatGPT dialogues to address claustrophobia in MRI - A radiographers' education perspective.

INTRODUCTION: The healthcare sector invests significantly in communication skills training, but not always with satisfactory results. Recently, generative Large Language Models, have shown promising results in medical education. This study aims to use ChatGPT to simulate radiographer-patient conversations about the critical moment of claustrophobia management during MRI, exploring how Artificial Intelligence can improve radiographers' communication skills. METHODS: This study exploits specifically designed prompts on ChatGPT-3.5 and ChatGPT-4 to generate simulated conversations between virtual claustrophobic patients and six radiographers with varying levels of work experience focusing on their differences in model size and language generation capabilities. Success rates and responses were analysed. The methods of radiographers in convincing virtual patients to undergo MRI despite claustrophobia were also evaluated. RESULTS: A total of 60 simulations were conducted, achieving a success rate of 96.7% (58/60). ChatGPT-3.5 exhibited errors in 40% (12/30) of the simulations, while ChatGPT-4 showed no errors. In terms of radiographers' communication during the simulations, out of 164 responses, 70.2% (115/164) were categorized as "Supportive Instructions," followed by "Music Therapy" at 18.3% (30/164). Experts mainly used "Supportive Instructions" (82.2%, 51/62) and "Breathing Techniques" (9.7%, 6/62). Intermediate participants favoured "Music Therapy" (26%, 13/50), while Beginner participants frequently utilized "Mild Sedation" (15.4%, 8/52). CONCLUSION: The simulation of clinical scenarios via ChatGPT proves valuable in assessing and testing radiographers' communication skills, especially in managing claustrophobic patients during MRI. This pilot study highlights the potential of ChatGPT in preclinical training, recognizing different training needs at different levels of professional experience. IMPLICATIONS FOR PRACTICE: This study is relevant in radiography practice, where AI is increasingly widespread, as it explores a new way to improve the training of radiographers.

Clinical variables associated with late-onset thrombotic and cardiovascular events, after SARS-CoV-2 infection, in a cohort of patients from the first epidemic wave: an 18-month analysis on the "Surviving-COVID" cohort from Bergamo, Italy.

Importance: Population studies have recorded an increased, unexplained risk of post-acute cardiovascular and thrombotic events, up to 1 year after acute severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Objectives: To search for clinical variables and biomarkers associated with late post-acute thrombotic and cardiovascular events after SARS-CoV-2 infection. Design: Retrospective cohort study. Setting: Third-level referral hospital in Bergamo (Italy). Participants: Analysis of an existing database of adult patients, who received care for SARS-CoV-2 infection at our institution between 20 February and 30 September 2020, followed up on a single date ("entry date") at 3-6 months. Exposure: Initial infection by SARS-CoV-2. Main outcomes and measures: Primary outcome: occurrence, in the 18 months after entry date, of a composite endpoint, defined by the International Classification of Diseases-9th edition (ICD-9) codes for at least one of: cerebral/cardiac ischemia, venous/arterial thrombosis (any site), pulmonary embolism, cardiac arrhythmia, heart failure. Measures (as recorded on entry date): history of initial infection, symptoms, current medications, pulmonary function test, blood tests results, and semi-quantitative radiographic lung damage (BRIXIA score). Individual clinical data were matched to hospitalizations, voluntary vaccination against SARS-CoV-2 (according to regulations and product availability), and documented reinfections in the following 18 months, as recorded in the provincial Health Authority database. A multivariable Cox proportional hazard model (including vaccine doses as a time-dependent variable) was fitted, adjusting for potential confounders. We report associations as hazard ratios (HR) and 95% confidence intervals (CI). Results: Among 1,515 patients (948 men, 62.6%, median age 59; interquartile range: 50-69), we identified 84 endpoint events, occurring to 75 patients (5%): 30 arterial thromboses, 11 venous thromboses, 28 arrhythmic and 24 heart failure events. From a multivariable Cox model, we found the following significant associations with the outcome: previous occurrence of any outcome event, in the 18 months before infection (HR: 2.38; 95% CI: 1.23-4.62); BRIXIA score ≥ 3 (HR: 2.43; 95% CI: 1.30-4.55); neutrophils-to-lymphocytes ratio ≥ 3.3 (HR: 2.60; 95% CI: 1.43-4.72), and estimated glomerular filtration rate < 45 ml/min/1.73 m2 (HR: 3.84; 95% CI: 1.49-9.91). Conclusions and relevance: We identified four clinical variables, associated with the occurrence of post-acute thrombotic and cardiovascular events, after SARS-CoV-2 infection. Further research is needed, to confirm these results.