{
  "abstract": "Laboratory tests are crucial for diagnosing and managing health conditions, offering essential reference ranges for result interpretation. The diversity of lab tests, influenced by variables such as the specimen type (e.g., blood, urine), gender, age, and other influencing factors like pregnancy, makes automated interpretation challenging. Automated clinical decision support systems attempting to interpret these values must account for such nuances to avoid misdiagnoses or incorrect clinical decisions. In this regard, we present LabQAR (Laboratory Question Answering with Reference Ranges), a manually curated dataset comprising 550 lab test reference ranges derived from authoritative medical sources, encompassing 363 unique lab tests and including multiple-choice questions with annotations on reference ranges, specimen types, and other factors impacting interpretation. We also assess the performance of several large language models (LLMs), including LLaMA 3.1, GatorTronGPT, GPT-3.5 turbo, GPT-4 turbo, and GPT-4o, in predicting reference ranges and classifying results as normal, low, or high. The findings indicate that GPT-4o outperforms other models, showcasing the potential of LLMs in clinical decision support.",
  "abstract_shown": true,
  "affiliations": [
    "School of Information, Florida State University, Tallahassee, Florida USA",
    "National Library of Medicine, Bethesda, Maryland USA",
    "Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida USA",
    "Department of Family Medicine, Morsani College of Medicine, University of South Florida, Tampa, Florida USA",
    "Institute for Successful Longevity, Florida State University, Tallahassee, Florida USA"
  ],
  "article_type": "data paper",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Balu Bhasuran",
      "orcid": "0000-0002-9890-4627"
    },
    {
      "affs": [
        1
      ],
      "name": "Qiao Jin",
      "orcid": "0000-0002-1268-7239"
    },
    {
      "affs": [
        0
      ],
      "name": "Angelique Deville"
    },
    {
      "affs": [
        2
      ],
      "name": "Yonghui Wu"
    },
    {
      "affs": [
        3
      ],
      "name": "Karim Hanna"
    },
    {
      "affs": [
        1
      ],
      "name": "Zhiyong Lu",
      "orcid": "0000-0001-9998-916X"
    },
    {
      "affs": [
        0,
        4
      ],
      "name": "Zhe He",
      "orcid": "0000-0003-3608-0244"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5rnygfyx4g05v.bib",
  "cited_by": 0,
  "doi": "10.1038/s41597-026-07554-z",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13720059/",
  "id": "5rnygfyx4g05v",
  "issns": [
    "2052-4463"
  ],
  "landing_url": "https://doi.org/10.1038/s41597-026-07554-z",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Data Descriptor"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7164148867",
  "pdf_url": "https://www.nature.com/articles/s41597-026-07554-z_reference.pdf",
  "pmcid": "PMC13720059",
  "pmid": "42270658",
  "published": "2026-06-10",
  "publisher": "Nature Publishing Group",
  "ris": "https://research.draconitic.com/paper/5rnygfyx4g05v.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T12:23:55Z",
      "native_id": "PMC13720059",
      "source": "pmc",
      "url": "https://doi.org/10.1038/s41597-026-07554-z"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "A Curated Dataset for Question Answering on Laboratory Test Reference Ranges and Interpretation",
  "title_text": "A Curated Dataset for Question Answering on Laboratory Test Reference Ranges and Interpretation",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T12:21:00Z",
  "url": "https://research.draconitic.com/paper/5rnygfyx4g05v",
  "venue": "Scientific Data",
  "versions": []
}