标题： RWESummary：一种框架和测试方法，用于选择大型语言模型来总结现实世界证据（RWE）研究 显示英文标题

标题： RWESummary: A Framework and Test for Choosing Large Language Models to Summarize Real-World Evidence (RWE) Studies

摘要： 大型语言模型（LLMs）已在一般摘要任务以及医学研究辅助方面进行了广泛评估，但尚未专门针对从真实世界证据（RWE）研究的结构化输出中总结真实世界证据的任务进行评估。我们引入了RWESummary，这是MedHELM框架（Bedi, Cui, Fuentes, Unell 等，2025）的一个提议补充，以实现对此任务的LLMs基准测试。RWESummary包括一个场景和三个评估，涵盖了医学研究摘要中观察到的主要类型错误，并使用Atropos Health专有数据开发而成。此外，我们使用RWESummary来比较不同LLMs在我们内部的真实世界证据摘要工具中的性能。在发表时，有13个不同的RWE研究，我们发现Gemini 2.5模型整体表现最佳（包括Flash和Pro版本）。我们建议RWESummary作为真实世界证据研究摘要的一种新颖且有用的基准模型。 显示英文摘要

摘要： Large Language Models (LLMs) have been extensively evaluated for general summarization tasks as well as medical research assistance, but they have not been specifically evaluated for the task of summarizing real-world evidence (RWE) from structured output of RWE studies. We introduce RWESummary, a proposed addition to the MedHELM framework (Bedi, Cui, Fuentes, Unell et al., 2025) to enable benchmarking of LLMs for this task. RWESummary includes one scenario and three evaluations covering major types of errors observed in summarization of medical research studies and was developed using Atropos Health proprietary data. Additionally, we use RWESummary to compare the performance of different LLMs in our internal RWE summarization tool. At the time of publication, with 13 distinct RWE studies, we found the Gemini 2.5 models performed best overall (both Flash and Pro). We suggest RWESummary as a novel and useful foundation model benchmark for real-world evidence study summarization.