Large-language models (LLMs) show promise for extracting information from clinical notes. Deploying these models at scale can be challenging due to high computational costs, regulatory constraints, an
本数据集包含CL4Health2026会议CRF填充共享任务的标注训练表格。临床笔记收集自意大利都灵San Giovanni Bosco医院，经过匿名化和标注处理。数据集包含两个语言分片：英语（en）和意大利语（it），共10个样本。每个样本包含：临床笔记标识符（document_id）、患者临床病史记录（clinical_note）以及带有真实标签的CRF项目标注（annotations）。
We present the SynSUM benchmark, a synthetic dataset linking unstructured clinical notes to structured background variables. The dataset consists of 10,000 artificial patient records containing t
