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Table 4 A summary of the performance of two ROC curve cut-offs that could be best used to predict the final diagnosis. One cut-off is chosen to give the best sensitivity and a second to give the optimum combination of sensitivity and specificity (overall accuracy)

From: Evaluating thyroid nodules: predicting and selecting malignant nodules for fine-needle aspiration (FNA) cytology

Method

Statistic

Estimate (95 % CI)

Regression results 1 (best sensitivity)

Sensitivity

1.00 (0.82, 1.00)

Specificity

0.75 (0.67, 0.82)

Positive predictive value

0.33 (0.21, 0.47)

Negative predictive value

1.00 (0.96, 1.00)

Overall accuracy

0.78 (0.71, 0.84)

Regression results 2 (best combination of sensitivity and specificity)

Sensitivity

0.95 (0.74, 1.00)

Specificity

0.81 (0.74, 0.87)

Positive predictive value

0.38 (0.25, 0.54)

Negative predictive value

0.99 (0.96, 1.00)

Overall accuracy

0.82 (0.76, 0.88)