Articles: pandemics.
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Chest-computer tomography (CT) is a crucial factor in the clinical course and evaluation of patients with COVID-pneumonia. In the initial phase of the COVID-19 pandemic little information was known on the prognostic value of the initially taken thoracic CTs. The purpose of this study was to determine predictive values for clinical outcome based on CT classification of the pulmonary pathologies in patients with COVID-pneumonia. ⋯ A classification system used in this study is helpful for classifying imaging features and is recommended as a standardized CT reporting tool. It could also help in triaging of the therapy of patients with COVID-19 pneumonia. Especially the comorbidities, diabetes and arterial hypertonia triggered a negative outcome in our study population.
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Global coronavirus disease 2019 pandemic leads to the soaring demand for medical statistical applications, bringing a great challenge to medical education at universities worldwide. The purpose of our study is to investigate medical students and teachers attitudes and demands on statistical software education. A multi-city cross-sectional study was conducted in 2021 at medical universities in eastern China. ⋯ Notably, very few students and teachers thought "Statistical software met needs" (from 21.8% of undergraduates to 8.8% of teachers). There were 75.4% of post-graduates and 96.5% of teachers who thought it was necessary for a university to offer an advanced statistical software curriculum such as the R package in the preferred teaching format of offline class as well as the combination of theory and software practice teaching. This study for the first time demonstrated that most medical undergraduates, post-graduates, and teachers in Anhui Province of eastern China were not satisfied with statistical software usage experience, calling for prompt adjustments to statistical software education in medical universities.
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Psychological and behavioral stress has increased enormously during Coronavirus Disease 2019 (COVID-19) pandemic. However, early prediction and intervention to address psychological distress and suicidal behaviors are crucial to prevent suicide-related deaths. This study aimed to develop a machine algorithm to predict suicidal behaviors and identify essential predictors of suicidal behaviors among university students in Bangladesh during the COVID-19 pandemic. ⋯ The performance evaluation and comparison of the MLM showed that all models behaved consistently and were comparable in predicting suicidal risk. However, the Support Vector Machine was the best and most consistent performing model among all MLMs in terms of accuracy (79%), Kappa (0.59), receiver operating characteristic (0.89), sensitivity (0.81), and specificity (0.81). Support Vector Machine is the best-performing model for predicting suicidal risks among university students in Bangladesh and can help in designing appropriate and timely suicide prevention interventions.