Bar plots displaying the mean test set ROC-AUC of models in classifying pain diagnoses, with error bars indicating the 95% CI, estimated from 1,000 bootstrap samples over five iterations of fivefold CV ( n = 25). Overlaid points correspond to AUC scores from individual validation folds ( n = 25 points total). The bars represent the highest ROC-AUC scores achieved, separated into biological (left) and psychosocial (right) modalities. Bubble heat maps show ROC-AUC scores for modality subcategories, where applicable; bubble colour indicates the absolute AUC score, and bubble size reflects the z score of the AUC relative to other diagnoses within a given modality or subcategory. Only diagnoses with z scores above zero, indicating performance above the group mean AUC, are shown. For clearer visualization, diagnoses are grouped by their best biological modality performance (that is, highest AUC score) into four categories, from poor [0.60–0.65 AUC) to excellent (0.75+ AUC).
