More and more systematic reviews of diagnostic test accuracy studies are being published,
but they can be methodologically challenging. In this paper, the authors present some of the …

Evaluating binary classifications is a pivotal task in statistics and machine learning, because
it can influence decisions in multiple areas, including for example prognosis or therapies of …

ROC curve analysis is often applied to measure the diagnostic accuracy of a biomarker. The
analysis results in two gains: diagnostic accuracy of the biomarker and the optimal cut-point …

Most epistemic uncertainty within data-driven landslide susceptibility assessment results
from errors in landslide inventories, difficulty in identifying and mapping landslide causes …

In biomedical research and practice, quantitative tests or biomarkers are often used for
diagnostic or screening purposes, with a cut point established on the quantitative …

Aim The area under the receiver operating characteristic (ROC) curve (AUC) is a widely
used statistic for assessing the discriminatory capacity of species distribution models. Here, I …

Early detection of autism, a neurodevelopmental condition associated with challenges in
social communication, ensures timely access to intervention. Autism screening …

The use of biomarkers is of ever-increasing importance in clinical diagnosis of disease. In
practice, a cutpoint is required for dichotomizing naturally continuous biomarker levels to …

The receiver operating characteristic (ROC) curve is used to evaluate a biomarker's ability
for classifying disease status. The Youden Index (J), the maximum potential effectiveness of …

The nutritional status of an individual or population needs to be assessed through valid and
reliable biomarkers. Cutoffs generally have an underlying relation to health status and are …