Extended Data Fig. 5: Biological and psychosocial cross-prediction models for pain diagnoses.

a, Models trained on specific diagnoses were evaluated for their ability to predict other diagnoses that they weren’t trained on based on averaged performance metrics (ROC-AUC, sensitivity, specificity) across untrained diagnoses. This cross-prediction analysis was conducted for the diagnoses that were most accurately classified using blood, brain, and bone modalities alongside psychosocial models. b, Average cross-prediction ROC-AUC curves are displayed for both biological and psychosocial models within each biological modality. c, Quadrant plots show the average sensitivity and specificity of cross-prediction for each diagnosis. Points within the plots are color-coded by modality and labeled by the diagnosis on which the model was trained. Here, sensitivity measures the model's accuracy in detecting untrained diagnoses, while specificity gauges its precision in identifying diagnosis-free controls.