TrialCode Agent: LLM-Assisted Clinical Code-Set Construction for Trial Emulation

Objective Translating free-text clinical trial criteria into computable code sets is a valuable standardization practice that is necessary for producing reproducible real-world evidence studies but requires standardized interpretation across multiple clinical vocabularies. Methods We developed TrialCode Agent, a hybrid-large language model (LLM)-terminology verification agent that generates, formats, verifies, and expands candidate codes from free-text clinical criteria. The system supports ICD-9-CM diagnoses and procedures, ICD-10-CM, ICD-10-PCS, LOINC, and RxNorm medication concepts. We compared Baseline, Hybrid biomedical retrieval-augmented generation (RAG), and terminology-guided Family expansion pipelines using Claude, GPT Qwen, and MedGemma on 40 criteria from 11 trial groups. Performance was evaluated against expert-built reference code sets using exact-code precision, recall, and F1. Results The optimal pipeline varied by model. Claude with Baseline achieved the highest performance (precision 0.755, recall 0.619, F1 0.680), followed by GPT-5.5 with Baseline (precision 0.569, recall 0.658, F1 0.610), Qwen with Hybrid biomedical RAG (precision 0.656, recall 0.470, F1 0.548), and MedGemma with Family expansion (precision 0.487, recall 0.316, F1 0.383). Hybrid biomedical RAG improved aggregate F1 only for Qwen but increased GPT-5.5 RxNorm F1 from 0.320 to 0.909. Macro-averaged results showed criterion-level gains despite lower micro-averaged aggregate performance. Family expansion increased recall across models but generally reduced precision. In staged verifier ablation, micro-F1 increased from 0.254 before verification to 0.505 after final verification and expansion. Existence/vocabulary checking removed 2,594 false-positive codes, and acceptance filtering removed 952 additional false-positive codes before controlled expansion. Conclusions Combining LLM-based clinical interpretation with deterministic terminology verification produces auditable, database-ready code sets, but retrieval and broad family expansion do not consistently improve exact-code performance. Retrieval was particularly useful for RxNorm mapping, whereas overly broad or incomplete candidate generation remained the main source of error. Deterministic verification improves code validity and query readiness but cannot replace accurate clinical interpretation.