证据过多，时间过少：通过多目标证据推理从文本到可操作的建议
Too much evidence, too little time: From text to actionable recommendations through multi-objective evidence reasoning
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中文总结 AI 辅助
该研究针对临床决策中证据过多难审查的问题，提出SCEPTER框架，结合多种技术将临床病例描述转化为建议，经150个案例研究评估，能大幅压缩搜索空间，留存证据多样，基于帕累托的选择提升了证据多样性和建议效用。
中文摘要 AI 辅助
基于证据的临床决策要求专家识别、评估和综合相关科学文献。然而，针对复杂临床病例在PubMed上的搜索常常返回数百篇出版物，在时间限制下无法手动审查。本研究提出了SCEPTER（单病例证据驱动的PubMed到推荐推理器）框架，用于将临床病例描述转化为基于证据的建议。SCEPTER结合了PubMed检索、PubMedBERT语义排序、基于大语言模型的主张提取、证据级别加权、矛盾检测、共识分析和多目标帕累托主张选择。该框架生成结构化的证据综合和有依据的可操作建议。一个论文问答模块进一步实现对所选出版物的交互式探索。对150个案例研究的评估表明，该框架将平均576篇论文的搜索空间减少到53篇留存论文、7个帕累托最优主张和3个最终建议，总体压缩比为192:1。尽管有这种减少，留存的证据仍保持高度多样性（熵=0.901）。消融研究表明，与传统排序方法相比，基于帕累托的选择增加了证据多样性和建议效用。
英文摘要
Evidence-based clinical decision making requires specialists to identify, evaluate and synthesize relevant scientific literature. However, PubMed searches for complex clinical cases often return hundreds of publications that cannot be reviewed manually under time constraints. This study proposes SCEPTER (Single-Case Evidence-driven PubMed-To-rEcommendation Reasoner), a framework for transforming clinical case descriptions into evidence-based recommendations. SCEPTER combines PubMed retrieval, PubMedBERT semantic ranking, large language model (LLM)-based claim extraction, evidence-level weighting, contradiction detection, consensus analysis and multi-objective Pareto claim selection. The framework generates structured evidence syntheses and grounded actionable recommendations. A Paper Q&A module further enables interactive exploration of selected publications. The proposed framework introduces multi-objective reasoning model that integrates literature support, contradiction analysis and interactive literature interrogation into a unified clinical decision-support pipeline. Evaluation on 150 case studies demonstrated that the framework reduced an average search space of 576 papers to 53 retained papers, 7 Pareto-optimal claims and 3 final recommendations, corresponding to an overall compression ratio of 192:1. Despite this reduction, the retained evidence maintained high diversity (entropy=0.901). The ablation study showed that Pareto-based selection increased evidence diversity and recommendation utility compared with conventional ranking approaches.
