A Reproducible Decomposed Large Language Model Pipeline for Structured Abstraction of Multimodal Neuroimaging Reports: Development and Independent Validation in 1,669 Emergency Department Encounters for Dizziness and Vertigo

Objective: To develop and independently validate a decomposed, modality-specific large language model (LLM) pipeline that abstracts structured variables from the free-text neuroimaging reports of a multimodal stroke workup, and to validate the resulting structured dataset for reuse in downstream research. Materials and Methods: A frozen pipeline (temperature 0) extracting 22 variables across CT head, CT angiography, CT perfusion, and MRI brain was applied to 1,669 ED encounters for dizziness or vertigo; 11 variables were pre-specified as primary (Tier 1). Reproducibility used three same-sessio

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