Sentence examples for classifications were assessed from inspiring English sources

Differences in ApoE levels between clinical classifications were assessed using ANOVA.

The agreement in classification of cases based on different hierarchical clustering methods (average linkage vs complete linkage) and different IHC classifications were assessed with the κ-statistic.

Discrimination of the classifications was assessed by the area under the receiver operator characteristic curve (AuROC).

Statistical significance of the described classifications was assessed by linear general models regression procedures (GLM).

The percentage correctly classified was calculated, and the agreement between repeated BMI classifications was assessed using a weighted Cohen's kappa coefficient.

Overall differences in the median excretion level between pigs with different scores of gross pathology, histopathology and IHC classifications was assessed by the Kruskal-Wallis equality of populations rank test.

Sensitivity, specificity, positive predictive value, negative predictive value, and total correct classification were assessed.

The number of intubation trials and Cormack's classification were assessed.

Range of motion (active and passive) and functional outcome (Strickland classification) were assessed.

Age, e-GFR at time of biopsy, and histopathologic classification were assessed as potential predictors of 12 month e-GFR using multivariate linear regression.

Tumour staging and histological classification were assessed according to TNM Classification of Malignant Tumours (Sobin and Wittekind, 2002) defined by the International Union Against Cancer UICCC) and the World Health Organization Histological Classification of Tumours (Hamilton and Altonen, 2000).