Clinical evidence synthesis requires identifying relevant trials from large registries and aggregating results that account for population differences. While recent LLM-based approaches have automated components of systematic review, they do not support end-to-end evidence synthesis. Moreover, conventional meta-analysis weights studies by statistical precision without considering clinical compatibility reflected in eligibility criteria. We propose EligMeta, an agentic framework that integrates automated trial discovery with eligibility-aware meta-analysis, translating natural-language queries into reproducible trial selection and incorporating eligibility alignment into study weighting to produce cohort-specific pooled estimates. EligMeta employs a hybrid architecture separating LLM-based reasoning from deterministic execution: LLMs generate interpretable rules from natural-language queries and perform schema-constrained parsing of trial metadata, while all logical operations, weight computations, and statistical pooling are executed deterministically to ensure reproducibility. The framework structures eligibility criteria and computes similarity-based study weights reflecting population alignment between target and comparator trials. In a gastric cancer landscape analysis, EligMeta reduced 4,044 candidate trials to 39 clinically relevant studies through rule-based filtering, recovering all 13 guideline-cited trials. In an olaparib adverse events meta-analysis across four trials, eligibility-aware weighting shifted the pooled risk ratio from 2.18 (95% CI: 1.71-2.79) under conventional Mantel-Haenszel estimation to 1.97 (95% CI: 1.76-2.20), demonstrating quantifiable impact of incorporating eligibility alignment. EligMeta bridges automated trial discovery with eligibility-aware meta-analysis, providing a scalable and reproducible framework for evidence synthesis in precision medicine.

翻译：临床证据综合需要从大型注册库中识别相关试验，并汇总考虑人群差异的结果。尽管近期基于大语言模型的方法已实现系统评价的自动化组件，但它们不支持端到端的证据综合。此外，传统荟萃分析根据统计精度对研究进行加权，而未考虑资格标准中反映的临床相容性。我们提出EligMeta——一个整合自动化试验发现与资格感知荟萃分析的主体化框架，将自然语言查询转化为可复现的试验选择，并将资格对齐纳入研究权重计算，从而生成队列特定的合并估计值。EligMeta采用分离大语言模型推理与确定性执行的混合架构：大语言模型从自然语言查询中生成可解释规则，并对试验元数据进行模式约束解析，而所有逻辑操作、权重计算和统计合并均采用确定性方式执行以确保可复现性。该框架结构化资格标准并计算基于相似性的研究权重，反映目标试验与对照试验之间的人群对齐程度。在胃癌领域景观分析中，EligMeta通过基于规则的过滤将4,044个候选试验缩减至39个临床相关研究，恢复了所有13个指南引用的试验。在奥拉帕利不良事件荟萃分析（涉及四项试验）中，资格感知加权将使传统Mantel-Haenszel估计下的合并风险比从2.18（95%置信区间：1.71-2.79）变为1.97（95%置信区间：1.76-2.20），展示了纳入资格对齐的可量化影响。EligMeta弥合了自动化试验发现与资格感知荟萃分析之间的鸿沟，为精准医学中的证据综合提供了可扩展且可复现的框架。