The DOI matches its DataCite record.
LabQAR (Lab oratory Q uestion A nswering with R eference Ranges) Data Set
Abstract
LabQAR (Lab oratory Q uestion A nswering with R eference Ranges), a manually curated dataset containing multiple-choice questions about 550 lab tests with comprehensive reference ranges sourced from trusted medical resources 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, GPT-4, and GPT-4o, in predicting reference ranges and classifying results as normal, low, or high.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cited by
Sorted by citations
A Curated Dataset for Question Answering on Laboratory Test Reference Ranges and Interpretation
Bhasuran, B., Jin, Q., Deville, A., Wu, Y., Hanna, K., Lu, Z., & He, Z. (2026) · Scientific Data · doi:10.1038/s41597-026-07554-z
Self-citation
Metadata
- Authors
- bhasuran, B., & He, Z.
- ORCID
- bhasuran, Balu
- Venue
- figshare
- Publisher
- figshare
- Published
- 2026
- References
- 0
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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