<p>The longitudinal axis represents sensitivity to predict the probability of POTS. The transversal axis represents the false positive rate (1-specificity) of the prediction. The 45° gray line of the graph stands for reference line, representing sensitivity being equal to false positive rate (e.g., does not have the predictive value completely). The red curve is farther from the reference line and nearer the upper left corner of the graph. Area under the curve was 0.839 (95% confidence interval: 0.786 to 0.891; p<0.001).</p
<p>ROC curve for the multiple logistic regression model for CMV serostatus using a set of selected p...
<p>Note: the threshold at fixed specificity/sensitivity achieved by doctors are used to calculate th...
<p>A. Predictive ability of the full and “conventional” models in the original population. ROC Curve...
<p>This figure shows the performance of the logistic regression model with the four predictive marke...
<p>The area under the ROC (AUC) = 0.997 (95% confidence interval [CI] 0.991 to 1.000, <i>P</i><0.001...
<p>A ROC curve plots the true positive rate (i.e., sensitivity) against the false positive rate (i.e...
<p>Based on the L1-regularized logistic regression model with six proteins, a receiver operating cha...
<p>Receiver Operating Characteristic curve (ROC) curve associated with the logistic regression model...
<p>Predictive variables for this equation are age, head AIS >3, PCS ≥6, GCS group, and TTS score wit...
Logistic regression is often used to find a linear combination of covariates which best discriminate...
<p>The colored curves represent the bins at 0.83 ppm (LDL1), 3.20 ppm (choline) and 2.30 ppm (acetoa...
<p>The blue line indicates the prediction scenario using only clinical variables (hippocampal sclero...
<p>FPR represents the false positive rate, and TPR is the true positive rate. The ROC curve is close...
<p>(A) ROC curves for each regression model. The diagonal reference line indicates performance accor...
<p>The x-axis is the false- positive rate, 1 –specificity; the y-axis is the true- positive rate, se...
<p>ROC curve for the multiple logistic regression model for CMV serostatus using a set of selected p...
<p>Note: the threshold at fixed specificity/sensitivity achieved by doctors are used to calculate th...
<p>A. Predictive ability of the full and “conventional” models in the original population. ROC Curve...
<p>This figure shows the performance of the logistic regression model with the four predictive marke...
<p>The area under the ROC (AUC) = 0.997 (95% confidence interval [CI] 0.991 to 1.000, <i>P</i><0.001...
<p>A ROC curve plots the true positive rate (i.e., sensitivity) against the false positive rate (i.e...
<p>Based on the L1-regularized logistic regression model with six proteins, a receiver operating cha...
<p>Receiver Operating Characteristic curve (ROC) curve associated with the logistic regression model...
<p>Predictive variables for this equation are age, head AIS >3, PCS ≥6, GCS group, and TTS score wit...
Logistic regression is often used to find a linear combination of covariates which best discriminate...
<p>The colored curves represent the bins at 0.83 ppm (LDL1), 3.20 ppm (choline) and 2.30 ppm (acetoa...
<p>The blue line indicates the prediction scenario using only clinical variables (hippocampal sclero...
<p>FPR represents the false positive rate, and TPR is the true positive rate. The ROC curve is close...
<p>(A) ROC curves for each regression model. The diagonal reference line indicates performance accor...
<p>The x-axis is the false- positive rate, 1 –specificity; the y-axis is the true- positive rate, se...
<p>ROC curve for the multiple logistic regression model for CMV serostatus using a set of selected p...
<p>Note: the threshold at fixed specificity/sensitivity achieved by doctors are used to calculate th...
<p>A. Predictive ability of the full and “conventional” models in the original population. ROC Curve...