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medRxiv

Fully Automated Abstraction of Longitudinal Breast Oncology Records with Off-The-Shelf Large Language Models

The study developed a HIPAA-compliant open-source pipeline in which off-the-shelf commercial large language models, without fine-tuning, abstracted variables from unnormalized, unlabeled, and unedited clinical notes, pathology reports, medication administration records, and demographics for 100 complex breast cancer patients (median chart over 3,100 pages, median 6.5 years of follow-up, median 7 lines of therapy), achieving high concordance with an expert oncologist for recurrence status (99%), germline BRCA1/2 pathogenic variants (100%), hormone receptor status (99%), HER2 status (96%), clinical stage (91%), PIK3CA mutation status (91%), and ESR1 mutation status (90%), approaching inter-oncologist variability for anti-cancer drug extraction, while exact therapy-line reconstruction remaine