The Predictive Accuracy of Methods Commonly Used for Evaluating Animal Distress

Accurate distress assessment is essential for ensuring animal welfare and scientific validity. Although behavioral and physiological indicators are commonly used, their predictive value across disease models and severity grades remains insufficiently defined. Here, we evaluated the discriminatory performance of body weight changes, distress scoring, burrowing, and nesting across ten datasets involving transmitter implantation, bile duct ligation (BDL), and chronic pancreatitis (CP) in mice. Receiver operating characteristic analyses determined the area under the curve (AUC), and optimal cut-offs derived from one dataset per model were applied to independent datasets to assess cross-project accuracy. Under moderate to high distress conditions, such as BDL and the acute phase after transmitter implantation, distress score showed excellent discriminative performance (AUC ≥ 0.96), while body weight, burrowing, and nesting also performed fair to excellent (AUC ≥ 0.76). In contrast, during low-distress conditions, including CP and recovery after implantation, discrimination was markedly reduced (body weight AUC ≥ 0.62; distress score, burrowing, and nesting AUC ≥ 0.50). When cut-offs were applied to independent datasets, distress score (≥ 96%) and body weight (≥ 86%) maintained high accuracy under moderate to high distress, whereas burrowing and nesting performed less reliably (≥ 65%). Accuracy declined further under low distress with body weight ≥ 56% and distress score ≥ 50%. Behavioral measures showed poor accuracy (burrowing ≥ 39%, nesting ≥ 19%). Overall, burrowing and nesting can support distress evaluation under moderate to high distress, but are less reliable than body weight and distress score, particularly under low-distress conditions.