In computational pathology, a common task is to analyze digitally scanned tissue samples to predict endpoints that enhance disease understanding and improve patient care.This often involves predicting clinical outcomes such as overall survival (OS) and progression-free survival (PFS), as well as biomarkers like microsatellite instability (MSI/MSS) status. A key requirement for deploying these models in real-world settings is their robustness to variations in preparation protocols (e.g. different stainings) and digitization (e.g., different scanners), ensuring reliability across different clinical centers. State-of-the-art computational pathology pipelines often rely on two components: a large, pre-trained representation model of image patches, also called foundation model, and a predictive model which associates the embedded patches to predicted outcome at the image level. In this work, we present how the robustness and generalizability of both components can be enhanced using model distillation and score calibration.