Linguistic Fidelity and Classification Performance of Large Language Models for Generating Synthetic Operative Notes: Evaluation Study
- DOI
- 10.2196/87276
- Published
- 2026-07-03
- Container
- JMIR Formative Research
- Publisher
- JMIR Publications Inc.
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.2196/87276,
title = {Linguistic Fidelity and Classification Performance of Large Language Models for Generating Synthetic Operative Notes: Evaluation Study},
author = {Meredith Cox and Elaine Lin and Nicholas Oleck and Carlee Jones and Neill Y Li and Suhail K Mithani and Alexander C Allori},
year = {2026},
journal = {JMIR Formative Research},
doi = {10.2196/87276},
url = {https://doi.org/10.2196/87276}
}RIS
TY - JOUR TI - Linguistic Fidelity and Classification Performance of Large Language Models for Generating Synthetic Operative Notes: Evaluation Study AU - Meredith Cox AU - Elaine Lin AU - Nicholas Oleck AU - Carlee Jones AU - Neill Y Li AU - Suhail K Mithani AU - Alexander C Allori PY - 2026 JO - JMIR Formative Research DO - 10.2196/87276 UR - https://doi.org/10.2196/87276 ER -
APA
Cox, M., Lin, E., Oleck, N., Jones, C., Li, N. Y., Mithani, S. K., & Allori, A. C. (2026). Linguistic Fidelity and Classification Performance of Large Language Models for Generating Synthetic Operative Notes: Evaluation Study. JMIR Formative Research. https://doi.org/10.2196/87276
Source records
- crossref · retrieved 2026-09-26T11:36:19.537Z