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After adjusting for many confounders, the multiple logistic regression model revealed that the levels of UHg-C and HHg were 2.047 and 5.396 times higher, respectively, in children with dental amalgam compared to those without (P < 0.01).
A multiple logistic regression model was developed in group A for DIC diagnosis.
A multiple logistic regression model was used for multivariate analysis.
2) Multiple logistic regression models A multiple logistic regression model was formulated stratifying by sex and assuming the presence or absence of dementia as a dependent variable.
A multiple nominal logistic regression model revealed that male sex, low M-value and high physical exercise (p-values <0.05) predicted development of IGT/T2DM.
The use of a binary logistic regression model revealed two SNPs that are predictive of AIEC phenotype (Table 5).
Binary logistic regression model reveals husband's approval of FP as one of the significant determinants of induced abortion.
Logistic regression models revealed no association between ZOL and delayed healing even after adjusting for other risk factors (OR, 1.21; 95% CI, 0.74-1.99; p = 0.44).
Multiple logistic regression model for induced abortion revealed higher odds with increasing age of the mother (Table 3).
A weighed multiple logistic regression model was applied.
A single multiple logistic regression model included all these covariates.
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