A Multi-Model LLM Consensus Framework to Identify EHR-Predictable Eligibility Criteria in NSCLC Immunotherapy Trials

Clinical trial participation in oncology remains low, with complex and granular eligibility criteria limiting timely accrual and excluding many patients who could otherwise benefit from novel therapies. We propose a structured framework that utilizes a multi-model large language model consensus pipeline to convert free-text eligibility from Phase III PD-1/PD-L1 non-small cell lung cancer trials into standardized umbrella concepts and to classify each concept into tiers of Electronic Health Record predictability. Applying this framework, we consolidated hundreds of heterogeneous eligibility traits into a manageable set of clinical concepts and found that roughly half are both clinically important and plausibly inferable from routine structured and unstructured EHR data, while the remainder either add little value to prediction or depend on specialized testing. This taxonomy provides a prioritized "predictable target" list for future NLP and machine-learning models and a practical blueprint for designing more computable, pragmatic eligibility criteria and EHR-driven prescreening tools.