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The sensitivity and specificity of variables retained by a multivariable analysis as being associated with neurological outcome was evaluated using receiving operating characteristic (ROC) curves with the corresponding area under the curve (AUC).
First, we seek to clarify this relationship by testing the two competing theories and enriching the specificity of variables included in analysis.
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We argue that scale-specificity of variables may depend on homogeneity of study areas and should be considered when exploring SOC dynamics.
The study has three specific aims: i) to explore degree of coverage of specified variables; ii) to investigate internal validity of data, including sensitivity and specificity of binary variables; and iii) to identify potential systematic errors.
Using at least one changed variable of the remaining three as the reference standard, the relative sensitivity and relative specificity of given variables in the diagnosis of PMI were as follows: ECG 67% and 100%, respectively; VCG 85%and94%4%; scintigraphy 92% and 97%; and serum enzymes 86%and96%6%.
We compare these rule-based simulation UGBMs to a null UGBM developed from the same data but lacking in specificity of predictive variables.
ROC curves were based on the sensitivity and specificity of significant variables in the prediction of awakening and are shown in Fig. 1.
Sensitivity and specificity of the variables were assessed using receiver operating characteristics (ROC).
ROC curves illustrated the sensitivity and specificity of selected variables via their AUC.
Third, parsimonious LUR models were created, because the specificity of model variables may be important for informing surveillance and regulation.
The sensitivity and specificity of these variables in grading brain tumors was 98.2% and 46.7%, respectively, and the AUC was 0.871.
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CEO of Professional Science Editing for Scientists @ prosciediting.com