用智能体框架自动筛选临床试验并按患者匹配度加权，提升精准医疗证据合成效果。
Eligibility-Aware Evidence Synthesis: An Agentic Framework for Clinical Trial Meta-Analysis
- 通过自然语言查询生成可解释规则，自动筛选相关临床试验。
- 在胃癌分析中从4044个候选研究缩减至39个有效研究，召回所有指南引用试验。
- 基于患者特征匹配度调整权重，使不良事件风险比从2.18降至1.97。
临床证据合成需从大型注册库中识别相关试验，并整合结果以反映人群差异。现有基于大模型的方法虽自动化了系统评价部分环节，但无法支持端到端证据合成。传统元分析仅按统计精度加权，未考虑纳入标准所体现的临床兼容性。本文提出EligMeta，一个融合自动化试验发现与适应性元分析的智能体框架，将自然语言查询转化为可复现的试验筛选流程，并在研究加权中引入人群匹配度，生成针对特定队列的合并估计值。该框架采用混合架构，分离大模型推理与确定性执行：大模型生成可解释规则并进行模式约束的试验元数据解析，而所有逻辑运算、权重计算和统计聚合均确定性执行以保证可复现性。框架对纳入标准进行结构化处理，并计算基于相似性的研究权重，反映目标组与对照组之间的群体一致性。在胃癌领域分析中，EligMeta将4,044个候选试验缩减至39个临床相关研究，成功恢复全部13个指南引用试验。在奥拉帕利不良事件跨四项试验的元分析中，采用适应性加权后，合并风险比从传统的Mantel-Haenszel估计值2.18（95% CI: 1.71-2.79）调整为1.97（95% CI: 1.76-2.20），验证了纳入人群匹配度带来的量化影响。EligMeta实现了自动化试验发现与适应性元分析的衔接，为精准医学中的证据合成提供了可扩展、可复现的解决方案。
原文摘要 · Abstract (English)
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.
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