Medicine
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The coronavirus disease 2019 (COVID-19), a dual threat to public physical and mental health, prompted an investigation into the psychological well-being of residents in low- to medium-risk areas of China during the initial stages of the pandemic. We administered WeChat-based questionnaire surveys and employed chi-square tests and multiple logistic regression to analyze correlations between residents' age, gender, education, symptoms, COVID-19 close contact history, information sources, and anxiety, depression, and attitudes toward lockdown measures. We received 10,433 valid questionnaires, revealing 26% anxiety and 19.5% depression. ⋯ Our findings underscore the need for increased psychological counseling, particularly for young females with lower educational backgrounds or self-suspected infection symptoms, to mitigate mild to moderate anxiety and depression in future epidemics or pandemics. The public, especially those of working age with doctorates or higher education, bears the highest risk of severe anxiety. Lockdown measures enjoy strong support in low- to medium-risk areas of China.
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The vast majority of intelligent diagnosis models have widespread problems, which seriously affect the medical staff judgment of patients' injuries. So depending on the situation, you need to use different algorithms, The study suggests a model for intelligent diagnosis of lung nodule images based on machine learning, and a support vector machine-based machine learning algorithm is selected. In order to improve the diagnostic accuracy of intelligent diagnosis of lung nodule images as well as the diagnostic model of lung nodule images. ⋯ MN are distinct from the other 2 types, non-small nodules and benign small nodules, which require further training to differentiate. This has great practical value in teaching practice. Therefore, building a machine learning-based intelligent diagnostic model for pulmonary nodules is of significant importance in helping to solve medical imaging diagnostic problems.
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The definition of "nonurgent emergency service visits" is visits to conditions for medical conditions that require attention but are not life-threatening immediately or severe enough to require urgent intervention. This study aims to investigate the reasons why patients choose to self-refer to the emergency service (ES) instead of their primary care health center for nonurgent complaints. The study was carried out in a tertiary hospital. ⋯ The main reasons underlying self-referred patients were classified into 4 themes: "urgency" (13.8%), advantages of ES (12.9%); disadvantages of primary care (25.1%), and other (45.9%). The most common reason patients self-refer to the ES was their belief in "being urgent" (61%). In this study, 26.8%, (n = 84) of the patients are not happy with their family physicians, while only 13.2% (N = 43) prioritize the ES advantages.
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Comparative Study
Comparison of the problem-solving performance of ChatGPT-3.5, ChatGPT-4, Bing Chat, and Bard for the Korean emergency medicine board examination question bank.
Large language models (LLMs) have been deployed in diverse fields, and the potential for their application in medicine has been explored through numerous studies. This study aimed to evaluate and compare the performance of ChatGPT-3.5, ChatGPT-4, Bing Chat, and Bard for the Emergency Medicine Board Examination question bank in the Korean language. Of the 2353 questions in the question bank, 150 questions were randomly selected, and 27 containing figures were excluded. ⋯ ChatGPT-4 showed the highest correct response rate for the higher-order questions at 76.5%, and Bard and Bing Chat showed the highest rate for the lower-order questions at 71.4%. The appropriateness of the explanation for the answer was significantly higher for ChatGPT-4 and Bing Chat than for ChatGPT-3.5 and Bard (75.6%, 68.3%, 52.8%, and 50.4%, respectively). ChatGPT-4 and Bing Chat outperformed ChatGPT-3.5 and Bard in answering a random selection of Emergency Medicine Board Examination questions in the Korean language.
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Autonomic imbalance predicts worse clinical outcomes in patients with heart failure (HF). Managing the variables affecting heart rate variability (HRV) might improve the clinical outcomes of patients with HF. This study aimed to investigate variables affecting HRV. ⋯ We also assessed the self-care behavior of patients and their caregivers' burden. Depression in family caregivers and self-care behavior of patients were independently associated with a decreased Hf (β-coefficient = 0.309, P = .039 and β-coefficient = -0.029, P = .047, respectively). Depression of family caregivers and self-care behavior of patients may affect HRV in patients with HF.