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The cut-off score yielding the highest sum of specificity and sensitivity was used as optimal cut-off score.
The optimal cut-off point was defined as the point on the ROC curve with the highest sum of specificity and sensitivity, minimizing the overall misclassification [ 28].
The highest sum of specificity and sensitivity was 1.17 at a SAPS II of 46, with a mortality of 38% versus 0%, p < 0.001.
Model sensitivity, specificity, and positive and negative likelihood ratios were also calculated, and the best model cut-off points were considered to be those that corresponded to the highest sum of specificity and sensitivity.
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Based on the ROC analysis, a cutoff score of 8 would have a sensitivity of 100% and a specificity of 68.4%, yielding the highest sum of sensitivity and specificity.
Therefore, the best value of T0 was identified as that point on the ROC curve nearest the top left coordinate (ie, high sensitivity without losing too much specificity) 31 (see figure 3), which would give the highest sum of sensitivity and specificity.
The optimal cutoff value of CRP was defined as the value associated with the highest sum of sensitivity and specificity −1 (Youden index).
From this, a cutoff for mortality based on NRBC was specified according to Youden's method (representing the highest sum of sensitivity and specificity) [17].
Our study showed that at a cutoff scores of 5 in the GAD-7 and 1 in the GAD-2 had the highest sum of sensitivity and specificity.
The value that maximized the vertical distance between ROC and diagonal line (representing the highest sum of sensitivity and specificity) was used for calculation of a cutoff value, as described elsewhere [17].
Cutoff values corresponded to the highest sum of sensitivity and specificity.
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