PubMed COVID-19 Clinical Care

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had an unprecedented impact on human health and highlights the need for genomic epidemiology studies to increase our understanding of virus evolution and spread and to inform policy

CONCLUSION: The study found a 28.8% prevalence of comorbid disease among homeless people placed in temporary shelters in Tshwane. In adjusted analysis, being South African, being female, black and white race, and injecting substances were associated

CONCLUSIONS: Clinicians used inpatient telehealth to support care of hospitalized pediatric patients during the coronavirus disease pandemic, particularly for mental health care and family meetings. These findings suggest ongoing opportunities for

No abstract

CONCLUSION: The high student satisfaction and acceptance of both didactic concepts leads to the conclusion that the simulated telehealth conversation is an adequate substitute for the simulation of a traditional face-to-face conversation with regard

CONCLUSION: Our study reveals unique sex-specific cardiac changes in collegiate athletes following SARS-CoV-2 infection and quarantine compared to controls. Despite a mild reduction in LVEF, which was only observed in the first weeks following

Introduction: During the COVID-19 pandemic, care shifted from exclusively telemedicine to hybrid models with in-person, video, and telephone visits. We explored how patient satisfaction and visit preferences have changed by comparing in-person versus

In Brazil, during the pandemic caused by COVID-19, screening for breast and cervical cancers was postponed or interrupted due to the prevailing health conditions. These neoplasms, however, are responsible for high morbidity and mortality among women

AbstractThere is tremendous heterogeneity in the severity of COVID-19 disease in the human population, and the mechanisms governing the development of severe disease remain incompletely understood. The emergence of SARS-CoV-2 variants of concern (VOC

CONCLUSIONS: The XGBoost model did not significantly outperform the LASSO model in predicting postoperative delirium. Furthermore, a parsimonious logistic model with a few important predictors achieved comparable performance to machine learning