{
  "annotation_id": "28383bfb-b462-4072-b9cf-033bfac63996",
  "date_created": "2026-02-17T05:53:23.750000Z",
  "date_modified": "2026-02-17T05:53:23.750000Z",
  "file_hash": "3be03faedb0446eead1566aaf044bc2470a092c96d381d069c5dbf1e9b24fbac",
  "private": false,
  "record": {
    "abstract": "Real-world clinical text-to-SQL requires reasoning over heterogeneous EHR tables, temporal windows, and patient-similarity cohorts to produce executable queries. We introduce CLINSQL, a benchmark of 633 expert-annotated tasks on MIMIC-IV v3.1 that demands multi-table joins, clinically meaningful filters, and executable SQL. Solving CLINSQL entails navigating schema metadata and clinical coding systems, handling long contexts, and composing multi-step queries beyond traditional text-to-SQL. We evaluate 22 proprietary and open-source models under Chain-of-Thought self-refinement and use rubric-based SQL analysis with execution checks that prioritize critical clinical requirements. Despite recent advances, performance remains far from clinical reliability: on the test set, GPT-5-mini attains 74.7% execution score, DeepSeek-R1 leads open-source at 69.2% and Gemini-2.5-Pro drops from 85.5% on Easy to 67.2% on Hard. Progress on CLINSQL marks tangible advances toward clinically reliable text-to-SQL for real-world EHR analytics.",
    "arxiv_id": "2601.09876",
    "authors": [
      "Yifei Shen",
      "Yilun Zhao",
      "Justice Ou",
      "Tinglin Huang",
      "Arman Cohan"
    ],
    "categories": [
      "cs.CL"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Patient-Similarity Cohort Reasoning in Clinical Text-to-SQL",
    "url": "https://arxiv.org/abs/2601.09876",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "b74769ab-a809-482c-a05a-c6420f6a5972",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}