id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-350016-yxf7ykva	Qin, Le	A predictive model and scoring system combining clinical and CT characteristics for the diagnosis of COVID-19	2020-07-01		.txt	text/plain	3907	199	46	RESULTS: Multivariate logistic regression analysis showed that history of exposure (β = 3.095, odds ratio (OR) = 22.088), leukocyte count (β = − 1.495, OR = 0.224), number of segments with peripheral lesions (β = 1.604, OR = 1.604), and crazy-paving pattern (β = 2.836, OR = 2.836) were used for establishing the predictive model to identify COVID-19-positive patients (p < 0.05). The main finding of the present study was that we managed to develop a risk prediction model for the presence of COVID-19 in patients presenting with signs and symptoms of pneumonia that was based on clinical, laboratory, and CT imaging findings in a training group of 118 patients, and comprised history of exposure to people infected with COVID-19, normal or decreased leukocyte count, a high number of lung segments with pathologic CT findings including peripheral dominance of lesions and presence of crazy-paving patterns as risk factors for COVID-19.	./cache/cord-350016-yxf7ykva.txt	./txt/cord-350016-yxf7ykva.txt