Optimizing prompting strategies improves large language model classification of pain- and fatigue-related functional impact in childhood cancer survivors.
- DOI
- 10.1038/s43856-026-01499-5
- Published
- 2026 Mar 25
- Container
- Communications medicine
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1038/s43856-026-01499-5,
title = {Optimizing prompting strategies improves large language model classification of pain- and fatigue-related functional impact in childhood cancer survivors.},
author = {Sim JA and Horan MR and Huang X and Kim M and Srivastava DK and Ness KK and Hudson MM and Baker JN and Huang IC},
year = {2026},
journal = {Communications medicine},
doi = {10.1038/s43856-026-01499-5},
url = {https://doi.org/10.1038/s43856-026-01499-5}
}RIS
TY - JOUR TI - Optimizing prompting strategies improves large language model classification of pain- and fatigue-related functional impact in childhood cancer survivors. AU - Sim JA AU - Horan MR AU - Huang X AU - Kim M AU - Srivastava DK AU - Ness KK AU - Hudson MM AU - Baker JN AU - Huang IC PY - 2026 JO - Communications medicine DO - 10.1038/s43856-026-01499-5 UR - https://doi.org/10.1038/s43856-026-01499-5 ER -
APA
JA, S., MR, H., X, H., M, K., DK, S., KK, N., MM, H., JN, B., & IC, H. (2026). Optimizing prompting strategies improves large language model classification of pain- and fatigue-related functional impact in childhood cancer survivors.. Communications medicine. https://doi.org/10.1038/s43856-026-01499-5
Source records
- pubmed · retrieved 2026-09-25T20:08:24.975Z