Ágoston Hamar, Daryan Mohammed, Alex Váradi, Róbert Herczeg, Norbert Balázsfalvi, Béla Fülesdi, István László, Lídia Gömöri, Péter Attila Gergely, Gabor Laszlo Kovacs, Krisztián Jáksó, Katalin Gombos
COVID-19 mortality prediction in Hungarian ICU settings implementing random forest algorithm
Scientific reports
crosssectional schoolbased study leopoldo rio grande sul state brazil evaluated association food insecurity overweight first grade students municipal elementary school system total 2369 students invited participate 847 examined 782 data available weight height dietary data obtained parent guardian food insecurity measured brazilian food insecurity scale ebia data weight height provided nutrition service municipal department education prevalence rates overweight food insecurity 381 451 respectively controlling potential confounders children food insecurity 22 lower odds overweight notwithstanding inverse association exposure outcome sample showed high rates food insecurity overweight revealing complex relationship indicating research needed understand robust public policies critical addressing conditions