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Classifying the lesion marker curve as one of the following: waving (integral value equal to null); rapidly descending (rapidly moves away); slowly descending; rapidly descending with bounce (where the final value varies more than 20% from the value of maximum attenuation); slowly descending with final bounce (Fig. 6) 9.
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Due to the large number of data points, the measured/experimental results are shown as lines while the modeling results are shown as marker curves.
In Fig. 5, it shows that T †(0)(R s ) (blue line-marker curves) and T ‡(0)(R s ) (red line-marker curves) increases significantly with K increasing while T (0)(R s ) (black curves) does not increase much.
The size marker standard curve was traced.
The predicted probabilities of diagnosis generated a 'combination marker' ROC curve for all three categories (Table 4).
While the AUC value close to 1 indicates an excellent predictive marker, a curve that lies close to the diagonal (AUC = 0.5) has no diagnostic utility.
After IV bolus injection, CL estimates from multiple plasma samples are calculated from the area under the plasma marker disappearance curve (AUC) from time equals zero to infinity, i.e. CL = dose/ AUC.
For each marker, ROC curves for CIN3+ were computed.
For each putative marker, ROC curves were generated to evaluate their discriminatory power.
For each marker ROC curves were used to derive cut-offs for sensitivity and specificity in distinguishing sepsis from non-infective SIRS.
For the qualitative analysis of two methylation markers, amplification curves above the baseline indicated presence of methylated DNA in the plasma samples.
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