分析剖宫产后再分娩咨询语料，发现医生对风险的表述方式影响患者决策。
Implicit Framing in Obstetric Counseling Notes: A Grounded LLM Pipeline on a VBAC-Eligible Cohort
- 用大语言模型从病历中提取真实语境证据，构建可比的VBAC候选人群。
- 发现剖宫产再分娩与重复剖宫产的咨询文本中，风险描述占比差异显著。
- 为临床沟通质量评估提供可复现的自动化分析框架，适合医疗人工智能研究者。
临床框架指临床信息的表达方式，会影响患者理解与决策，对医疗结果有重要影响。产科是高风险领域，医生需向患者说明剖宫产后再分娩（VBAC）和重复剖宫产（RCS）的选择，但此类咨询语言在大规模临床文本分析中仍缺乏研究。本研究分析了2,024份严格定义的VBAC可行患者群体的产科病史与体格检查叙述。为排除医学禁忌症的干扰，我们基于结构化数据与大语言模型（LLM）驱动的抽取管道，仅使用自由文本中的可验证原话构建了VBAC-eligible队列。随后采用零样本大语言模型框架，将咨询片段分类至预设的框架类别，以捕捉医生如何语言呈现分娩选项。分析显示，VBAC与RCS记录中咨询框架分布存在显著差异：在RCS文档中，以风险为导向的语言占更大比例，类别层面差异经统计检验确认，凸显受控的LLM框架分析在产科护理中的价值。
原文摘要 · Abstract (English)
Clinical framing -- the linguistic manner in which clinical information is presented -- can influence patient understanding and decision-making, with important implications for healthcare outcomes. Obstetrics is a high-stakes domain in which physicians counsel patients on delivery mode choices such as vaginal birth after cesarean (VBAC) and repeat cesarean section (RCS), yet counseling language remains underexplored in large-scale clinical text analysis. In this work, we analyze physician counseling language in 2,024 obstetric history and physical narratives for a rigorously defined cohort of patients for whom both VBAC and RCS were clinically viable options. To control for confounding due to medical contraindications, we first construct a VBAC-eligible cohort using structured clinical data supplemented by a large language model (LLM)-based extraction pipeline constrained to grounded, verbatim evidence from free-text narratives. We then apply a zero-shot LLM framework to categorize counseling segments into predefined framing categories capturing how physicians linguistically present delivery options. Our analysis reveals a significant difference in counseling framing distributions between VBAC and RCS notes; risk-focused language accounts for a substantially larger share of counseling segments in RCS documentation than in VBAC, with category-level differences confirmed by statistical testing, highlighting the value of controlled LLM-based framing analysis in obstetric care.
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