{
  "id": 19477,
  "url": "https://arxiv.org/abs/2608.12477v1",
  "title": "Learning Under Treatment-Induced Label Indeterminacy with Expert Annotations of Counterfactual Outcomes: A Case Study in Neurological Prognostication",
  "summary": "Clinical prediction models are often developed as if the outcome of interest were cleanly observed for every patient. This assumption fails when treatment decisions make the clinically relevant outcome permanently unobservable. As a case study of this problem, we consider post-cardiac-arrest neurological prognostication using a cohort of 2,497 patients, including 1,429 patients whose outcomes were rendered indeterminate by treatment decisions. These patients with indeterminate outcomes were revi",
  "authors": "Xiaobin Shen, Chloe Y. H. Huang, Jonathan Elmer, George H. Chen",
  "category": "research",
  "topics": "healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-12T18:01:13.000Z",
  "fetched_at": "2026-08-14T05:10:49.168Z",
  "source_slug": "arxiv-cslg",
  "source_name": "arXiv cs.LG",
  "source_homepage": "https://arxiv.org/list/cs.LG/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/19477",
  "original_url": "https://arxiv.org/abs/2608.12477v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}