Understanding diagnostic tests 3: receiver operating characteristic curves

By A. Akobeng. Acta paediatrica, 2007

1,774 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Receiver operating characteristic curve: overview and practical use for clinicians (2022, background): “Therefore, the distance between the coordinates (0, 1) and the ROC curve should be minimized [25,26].”
Using opportunistic citizen science data to estimate avian population trends (2018, background): “Therefore, (0,1) is the idealized location for a test to fall in a sensitivity-specificity analysis, as it would amount to 100% accurate detection of both increasing and decreasing population trends (Akobeng, 2007).”
A cloud-compatible bioinformatics pipeline for ultrarapid pathogen identification from next-generation sequencing of clinical samples (2014, methodology): “Youden’s indexand the F 1 score (harmonic mean) were applied as independent criteria to select an optimal cutoff point of diagnostic accuracy for the ROC curve (Akobeng 2007).”
Reliability and validity of K10 and K6 in screening depressive symptoms in Hong Kong adolescents (2014, methodology): “The sensitivity, specificity, high negative predictive values, and high total classification accuracy of K10 and K6 provide evidence that they are reliable screening tools for identifying adolescents who may be suffering from SDS (Akobeng, 2007).”