{
  "id": 17398,
  "url": "https://arxiv.org/abs/2608.05203v1",
  "title": "From Continuous Predictors to Clinical Thresholds: Early Evidence on Performance Trade-offs of Guideline-Based Categorisation for Ischaemic Stroke Outcome Prediction",
  "summary": "Machine learning models achieve strong predictive accuracy for 90-day outcome prediction in acute ischaemic stroke, yet clinical adoption is limited by the misalignment of model explanations with clinicians' reasoning. Motivated by a clinician user study calling for clinical guideline-aligned cut-offs, we ask whether continuous predictors can be replaced by clinically informed categorical encodings without sacrificing performance. On a multi-centre European registry stratified into three treatme",
  "authors": "Esra Zihni, Katryna Cisek, Hamzah Ziadeh, Hendrik Knoche, Robert Mikulik, John D. Kelleher",
  "category": "research",
  "topics": "safety-alignment,healthcare",
  "orgs": null,
  "regions": "eu",
  "published_at": "2026-08-05T06:59:11.000Z",
  "fetched_at": "2026-08-07T05:10:58.501Z",
  "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/17398",
  "original_url": "https://arxiv.org/abs/2608.05203v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}