Patterns of Locus of Control in People Suffering from Heart Failure: An Approach by Clustering Method.

The assumption of responsibility in dealing with chronic diseases is of relevance in a resource-oriented and not only deficit-oriented medicine, especially in dealing with chronic diseases, including patients with chronic heart failure. The aim of the present study is to examine, based on the model of "locus of control", whether there are different patterns that would be relevant for a more targeted education and support of self-management in dealing with heart failure. For this purpose, a sample (n = 758) from 11 Polish cardiology centers have been assessed using the standardized self-assessment scale Multidimensional Health Locus of Control (MHLC), consisting of three dimensions: (i) internal localization of health control; (ii) external control by powerful others; (iii) external control by chance.

An Artificial Intelligence Approach to Guiding the Management of Heart Failure Patients Using Predictive Models: A Systematic Review.

Heart failure (HF) is one of the leading causes of mortality and hospitalization worldwide. The accurate prediction of mortality and readmission risk provides crucial information for guiding decision making. Unfortunately, traditional predictive models reached modest accuracy in HF populations.

The relationship between knowledge about the pandemic and willingness to get vaccinated against SARS-CoV-2 in medical students in Poland: A cross-sectional survey.

The COVID-19 pandemic underlines the importance of targeting the groups with the highest risk of vaccine hesitancy, understanding their fears, and alleviating them. As the pandemic situation is very dynamic due to the appearance of new SARS-CoV-2 variants, concerns might also change over time. This is the first study to evaluate the vaccination rate and state of knowledge among medical students in Poland, comparing English and Polish divisions.

Novel Phenotyping for Acute Heart Failure-Unsupervised Machine Learning-Based Approach.

Acute heart failure (AHF) is a life-threatening, heterogeneous disease requiring urgent diagnosis and treatment. The clinical severity and medical procedures differ according to a complex interplay between the deterioration cause, underlying cardiac substrate, and comorbidities. This study aimed to analyze the natural phenotypic heterogeneity of the AHF population and evaluate the possibilities offered by clustering (unsupervised machine-learning technique) in a medical data assessment.

Identification of risk groups for mental disorders, headache and oral behaviors in adults during the COVID-19 pandemic.

The dramatically changing situation during COVID-19 pandemic, is anticipated to provoke psycho-emotional disturbances and somatization arising from the current epidemiological situation that will become a significant problem for global and regional healthcare systems. The aim of this study was to identify the predictors, risk factors and factors associated with mental disorders, headache and potentially stress-modulated parafunctional oral behaviors among the adult residents of North America and Europe as indirect health effects of the COVID-19 pandemic. This may help limit the long-term effects of this and future global pandemic crises.