{
  "id": 4525,
  "url": "https://arxiv.org/abs/2605.11284v1",
  "title": "Rethinking external validation for the target population: Capturing patient-level similarity with a generative model",
  "summary": "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 introduces a framework to distinguish model deficiencies from case-mix effects. 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 di",
  "authors": "Mohammad Azizmalayeri, Ameen Abu-Hanna, Saskia Houterman, Marije M. Vis, Giovanni Cinà",
  "category": "research",
  "topics": "safety-alignment,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-11T22:11:03.000Z",
  "fetched_at": "2026-07-14T16:31:03.580Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4525",
  "original_url": "https://arxiv.org/abs/2605.11284v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}