Rethinking external validation for the target population: Capturing patient-level similarity with a generative model

ArXi:2605.11284v1 Announce Type: cross Background: External validation is essential for assessing the transportability of predictive models. However, its interpretation is often confounded by differences between external and development populations. This study Method: We propose a framework that quantifies each external patient's similarity to the development data and measures performance in subgroups with varying levels of alignment to the development distribution.