Areas under the curve (AUC)-values (95% CI) are 0.7679 (95% CI 0.64768 to 0.88812), 0.8648 (95% CI 0.79675 to 0.93278), 0.7584 (95% CI 0.65889 to 0.85782), 0.8664 (95% CI 0.79452 to 0.93834), and 0.7183 (95% CI 0.57795 to 0.85862) for SCORE*1.5 (A), CUORE*1.5 (B), FRS*1.5 (C), QRISK2-RA (D), and RRS*1.5 (E), respectively.</p
<p>ROC curve and the area under the curve (AUC) under different distributional assumptions for the B...
<p>The areas under the ROC curve (AUCs) for the CIRP level, the APACHE II score, the SOFA score, the...
The ROC curves in the training (A), internal validation (B) and external validation (C) groups. The ...
Areas under the curve (AUC)-values (95% CI) are 0.7679 (95% CI 0.64768 to 0.88812), 0.864 (95% CI 0....
<p>The area under the ROC curve (AUC) for each score and its 95% confidence interval are provided.</...
<p>AUC for R1 = 0.417, SE = 0.109; AUC for R2 = 0.683, SE = 0.095; AUC for ΔR = 0.831, SE = 0.077. T...
<p>ROC curves are plots of sensitivity and specificity of algorithms for distinguishing normal contr...
<p>The ROC curve for predicting TPS score >5 in fig 3 (left), the AUC for TPS score >5 was 0.73 (95%...
<p>For GRACE score alone, the area under the curve (AUC) was 0.749 (95% CI: 0.707–0.787). When RDW w...
Area under the ROC curve sensitivity = 77.5%, specificity = 61.3%, AUC = 0.751 with cutoff = 0.488.<...
5 × 5-fold cross-validation results: Mean ROC curves and AUC scores (95% C.I.).</p
<p>This translated into a sensitivity = 88% and specificity = 72% for discriminating between progres...
<p>The ROC curve for leave-one-out cross validation and the AUC of our algorithm is 0.7645.</p
<p>ROC curves and area under ROC curve (AUC) values can be used as more robust measures of classifie...
(A) Performance of the model in the training set, which showed an AUC value of 0.768, an optimal cut...
<p>ROC curve and the area under the curve (AUC) under different distributional assumptions for the B...
<p>The areas under the ROC curve (AUCs) for the CIRP level, the APACHE II score, the SOFA score, the...
The ROC curves in the training (A), internal validation (B) and external validation (C) groups. The ...
Areas under the curve (AUC)-values (95% CI) are 0.7679 (95% CI 0.64768 to 0.88812), 0.864 (95% CI 0....
<p>The area under the ROC curve (AUC) for each score and its 95% confidence interval are provided.</...
<p>AUC for R1 = 0.417, SE = 0.109; AUC for R2 = 0.683, SE = 0.095; AUC for ΔR = 0.831, SE = 0.077. T...
<p>ROC curves are plots of sensitivity and specificity of algorithms for distinguishing normal contr...
<p>The ROC curve for predicting TPS score >5 in fig 3 (left), the AUC for TPS score >5 was 0.73 (95%...
<p>For GRACE score alone, the area under the curve (AUC) was 0.749 (95% CI: 0.707–0.787). When RDW w...
Area under the ROC curve sensitivity = 77.5%, specificity = 61.3%, AUC = 0.751 with cutoff = 0.488.<...
5 × 5-fold cross-validation results: Mean ROC curves and AUC scores (95% C.I.).</p
<p>This translated into a sensitivity = 88% and specificity = 72% for discriminating between progres...
<p>The ROC curve for leave-one-out cross validation and the AUC of our algorithm is 0.7645.</p
<p>ROC curves and area under ROC curve (AUC) values can be used as more robust measures of classifie...
(A) Performance of the model in the training set, which showed an AUC value of 0.768, an optimal cut...
<p>ROC curve and the area under the curve (AUC) under different distributional assumptions for the B...
<p>The areas under the ROC curve (AUCs) for the CIRP level, the APACHE II score, the SOFA score, the...
The ROC curves in the training (A), internal validation (B) and external validation (C) groups. The ...