Current methods of calculating risk probability in universal screening do not fully account for sources of uncertainty in risk estimation. Bayesian ordinal regression models (BORMs) have promise in advancing recent research on post-test probabilities using interval likelihood ratios (Klingbeil et al., 2019; 2021) in the context of universal screening classification accuracy. BORMs can flexibly account for ordinality in the predictor and criterion variable, and they can fully account for the prior uncertainty in classification accuracy, providing easy-to- interpret probabilistic predictions for post-test probabilities. Through simulations and an applied example using real screening data, we elucidate some of the issues around ordinal regression models for application in screening, including potential strengths (e.g., multilevel modeling) and issues (difficulty of interpretation/implementation) related to these methods. We discuss how BORMs can further advance both research and practice of data-based decision making in universal screening in schools.