RASC+: Retrieval-Constrained LLM Adjudication for Clinical Value Set Authoring

The article introduces a novel approach called Retrieval-Constrained LLM Adjudication for authoring clinical value sets, addressing limitations in zero-shot LLM generation for clinical code systems. By utilizing a Qwen3-based retrieval mechanism with vocabulary-aware expansion, the candidate-pool recall improved from 0.553 to 0.730, while the integration of GPT-5 for adjudication significantly enhanced macro F1 scores from 0.287 to 0.549. This method is crucial for practitioners as it demonstrates a reliable framework for improving the accuracy and safety of clinical code retrieval in quality measurement and decision support applications.