Evaluating Large Language Models for Automated Clinical Abstraction in Pulmonary Embolism Registries: Performance Across Model Sizes, Versions, and Parameters

Pulmonary embolism (PE) is a leading cause of cardiovascular mortality, yet our understanding of optimal management remains limited due to heterogeneous and inaccessible radiology documentation. The PERT Consortium registry standardizes PE management data but depends on resource-intensive manual abstraction. Large language models (LLMs) offer a scalable alternative for automating concept extraction from computed tomography PE (CTPE) reports. This study evaluated the accuracy of LLMs in extracting PE-related concepts compared to a human-curated criterion standard. We retrospectively analyzed MIMIC-IV and Duke Health CTPE reports using multiple LLaMA models. Larger models (70B) outperformed smaller ones (8B), achieving kappa values of 0.98 (PE detection), 0.65-0.75 (PE location), 0.48-0.51 (right heart strain), and 0.65-0.70 (image artifacts). Moderate temperature tuning (0.2-0.5) improved accuracy, while excessive in-context examples reduced performance. A dual-model review framework achieved >80-90% precision. LLMs demonstrate strong potential for automating PE registry abstraction, minimizing manual workload while preserving accuracy.