临床事件时间锚定：保留UID的多模态重建与来源校准
Anchoring Clinical Events in Time: UID-Preserving Multimodal Reconstruction and Source-Grounded Adjudication
这是篇关于如何用GLM 5.2模型处理临床时间线数据的论文，能提高事件恢复率，比纯文本版本更好。
我们提出一种保留唯一标识符（UID）的框架，将叙事事件与来源文本片段关联，并通过文本估计、结构化证据检索、时间戳源行校准和联合修订来保留其身份。我们还提出GAVEL，一个LLM裁判，比较两条UID对齐的时间线与叙事和结构化记录。在六种开源模型和40份混合重症监护摘要上，GLM 5.2的多模态修订版相比其纯文本版本，在时间一致性上有所提升，事件召回率未下降，并表现出与临床注释相当的性能，而其他模型的改进较小且整体性能较低。盲评证实了大多数GAVEL发现，受控校准表明GLM 5.2的多模态版本优于纯文本版本，但DeepSeek V3.2则不然。
Anchoring Clinical Events in Time: UID-Preserving Multimodal Reconstruction and Source-Grounded Adjudication
Clinical timelines support treatment-window analysis and leakage-free modeling, but discharge summaries often obscure chronology and structured EHR tables describe only part of the patient course. We present a UID-preserving framework that links each narrative event occurrence to its source span and retains that identity through text-only estimation, structured-evidence retrieval, timestamped source-row grounding, and joint revision. We also present GAVEL, an LLM judge that compares two UID-aligned timelines against the narrative and structured record, to augment prior matching and temporal assessments. Across six open-weight models and 40 mixed-critical-care summaries, the GLM 5.2 multimodal revision, as compared to its text-only variant, improved temporal agreement without reducing event recovery and performed competitively with clinician annotations, while other model revisions showed smaller gains and lower overall performance. Ablations showed that UIDs primarily preserve event retention, whereas source-row linkage supports temporal placement. Blinded human review upheld most GAVEL findings, and controlled adjudication favored multimodal over text-only GLM 5.2 but did not for DeepSeek V3.2. In developing the UID and judge pipeline, we are able to demonstrate 43\% increased event recovery, a framework competitive with clinician annotations, and a system with occurrence-level provenance for both reconstruction and evaluation.
