Patient-specific rubric-based evaluation of large language models across longitudinal cancer care· NPC-RubricBench:基于患者病程的大型语言模型评估
Large language models (LLMs) show promise in specialty medicine, yet existing evaluations largely focus on isolated clinical encounters rather than tracking how clinical responsibilities evolve across a longitudinal care pathway. Here we introduce NPC-RubricBench, a phase-aware benchmark for evaluating multistage clinical reasoning across the cancer care continuum, using nasopharyngeal carcinoma as a defined disease context. Curated from longitudinal records of 405 patients across two cancer centers, the benchmark comprises 941 patient-phase units across six sequential care phases and 17,688 patient-specific atomic criteria spanning five evaluation dimensions, with each assessment strictly restricted to decision-time clinical evidence. Evaluating 30 general-purpose and medical LLMs reveale
使用患者病程数据评估大型语言模型在癌症护理中的多阶段临床推理能力
- 核心方法
- 构建了 NPC-RubricBench 基准,基于405名患者的纵向记录,涵盖六个护理阶段和17,688个患者特定的原子评估标准
- 适合谁读
- 研究者、工程师、临床医生
- 要解决的问题
- 现有的大型语言模型评估主要集中在单一临床接触点,缺乏对患者整个病程中临床责任演变的跟踪
- 关键实验
- 评估了30个通用和医疗领域的大型语言模型,揭示了模型在不同护理阶段的性能差异
- 主要贡献
- 提供了首个用于评估大型语言模型在癌症护理连续阶段中多阶段临床推理的基准工具
- 意义与局限
- 有助于提高大型语言模型在癌症护理中的应用,但目前仍需更多实际临床验证