质量行动保证：虚拟现实客观结构化临床考试中考官声明的多模态验证
Quality Action Assurance: Multimodal Verification of Examiner Claims in VR OSCEs
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中文总结 AI 辅助
研究针对VR儿科OSCEs中考官评分受主观因素影响问题，提出多模态框架QAA，结合约束时间动作对齐模型与大语言模型，通过对比动作与真实事件序列验证考官声明，提升了评估的公平性。
中文摘要 AI 辅助
客观结构化临床考试（OSCEs）是评估临床能力的黄金标准，但评分容易受到考官主观性、疲劳和认知偏差的影响。通过评分者间统计进行的标准考官验证缺乏对误差来源的解释力。因此，我们引入了质量行动保证（QAA），这是一个多模态框架，通过比较考官声称的动作与由视频、VR日志和参与者数据构建的真实事件序列，在虚拟现实（VR）儿科OSCEs中验证考官声明。QAA结合了一个约束时间动作对齐模型和一个大语言模型，在5折交叉验证中，QAA在时间对齐方面实现了99.2%±0.7%的参与者F1和93.4%±1.9%的W@16。总体而言，QAA检测考官错误的精度为70.0%，召回率为76.7%，将事实正确性从39.2%提高到79.2%，实现了更公平的OSCE评估。
英文摘要
Objective Structured Clinical Examinations (OSCEs) are the gold standard for assessing clinical competence, yet scoring remains vulnerable to examiner subjectivity, fatigue, and cognitive bias. Standard examiner validation via inter-rater statistics lacks explanatory power regarding the source of errors, as it neither analyzes examiner reasoning nor verifies examiner claims against actual events. Thus, we introduce Quality Action Assurance (QAA), a multimodal framework that verifies examiner claims in Virtual Reality (VR) pediatric OSCEs by comparing actions claimed by examiners against a reference record of events constructed from video, VR logs, and actor annotations. QAA combines a constrained temporal action alignment model, which performs action localization and actor source attribution, with a large language model that extracts examiner claims and checks them against the record. Across a 5-fold cross-validation, QAA achieves 99.2\% $\pm$ 0.7\% Actor F1 and 93.4\% $\pm$ 1.9\% W@16 for temporal alignment. Overall, QAA detects examiner errors with 69.9\% precision and 76.7\% recall; in retrospective evaluation, correcting the detected errors raises the share of factually correct transcripts from 39.2\% to 79.2\%, supporting fairer OSCE quality assessment.
