Of the nearly four million births that occur each year in the United States, almost one in three is a cesarean delivery. Despite the increasing C-section rate over the years, there is no evidence that the increase has caused a decrease in neonatal or maternal mortality or morbidity. Bayesian decision analysis is used to model the decision between classifying a patient as “failure-to-progress,” which is cause for a C-section, using current information (prior probability) or information gathered (posterior probability) as labor continues. The Bayesian decision models determine the conditions under which it is appropriate to gather additional information (i.e., take an observation) before deciding to end labor and perform a C-section based on the decision maker’s belief about successful labor. During an observation period, the decision maker learns more about the patient and her medical state and the likelihood of a successful vaginal delivery is updated. This study determines the conditional value of inform...

中文翻译：

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探索劳动中等待的价值

在美国每年发生的近400万例分娩中，几乎有三分之一是剖腹产。尽管多年来剖腹产的比率不断增加，但没有证据表明这种增加导致了新生儿或产妇死亡率或发病率的降低。贝叶斯决策分析用于对在将患者分类为“进展失败”（这是剖腹产的原因）之间进行决策的模型，使用当前信息（先验概率）或随着分娩继续而收集的信息（后验概率）。贝叶斯决策模型确定了在决定终止人工并根据决策者对成功劳动的信念进行剖腹产之前收集其他信息（即进行观察）的适当条件。在观察期间 决策者可以了解有关患者及其医疗状况的更多信息，并且可以更新成功进行阴道分娩的可能性。这项研究确定了信息的条件价值。