Abstract：
The past 20 years witnessed an invigoration of research on labor progression and a change of thinking regarding normal labor. New evidence is emerging, and more advanced statistical methods are applied to labor progression analyses. Given the wide variations in the onset of active labor and the pattern of labor progression, there is an emerging consensus that the definition of abnormal labor may not be related to an idealized or average labor curve. Alternative approaches to guide labor management have been proposed; for example, using an upper limit of a distribution of labor duration to define abnormally slow labor. Nonetheless, the methods of labor assessment are still primitive and subject to error; more objective measures and more advanced instruments are needed to identify the onset of active labor, monitor labor progression, and define when labor duration is associated with maternal/child risk. Cervical dilation alone may be insufficient to define active labor, and incorporating more physical and biochemical measures may improve accuracy of diagnosing active labor onset and progression. Because the association between duration of labor and perinatal outcomes is rather complex and influenced by various underlying and iatrogenic conditions, future research must carefully explore how to integrate statistical cut-points with clinical outcomes to reach a practical definition of labor abnormalities. Finally, research regarding the complex labor process may benefit from new approaches, such as machine learning technologies and artificial intelligence to improve the predictability of successful vaginal delivery with normal perinatal outcomes.
摘要：
在过去的20年中，有关劳动进展的研究得到了蓬勃发展，有关正常劳动的思想也发生了变化。新的证据正在出现，更先进的统计方法被应用于劳动进展分析。鉴于积极分娩的开始和分娩进展的模式差异很大，有一个新兴的共识，即非正常劳动的定义可能与理想化或平均的劳动曲线无关。已经提出了指导劳动管理的替代方法；例如，使用劳动持续时间分布的上限来定义异常缓慢的劳动。尽管如此，劳动评估的方法仍然很原始，容易出错；需要更客观的措施和更先进的工具来识别积极劳动的开始，监测分娩进展，并定义分娩时间与孕产妇/儿童风险相关的时间。单独的宫颈扩张可能不足以定义主动分娩，纳入更多的物理和生化措施可能会提高诊断积极分娩开始和进展的准确性。因为分娩时间和围产期结局之间的关系相当复杂，并且受各种潜在和医源性条件的影响，未来的研究必须仔细探索如何将统计学分界点与临床结局相结合,以达到分娩异常的实际定义.最后,关于复杂劳动过程的研究可能会受益于新的方法，例如机器学习技术和人工智能，以提高成功的阴道分娩与正常围产期结局的可预测性。
