ReviewAmerican journal of obstetrics and gynecology2023
The evolution of the labor curve and its implications for clinical practice: the relationship between cervical dilation, station, and time during labor.
Review in American journal of obstetrics and gynecology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07430358 (Obstetric Risk Assessment & Cesarean-delivery in Labor Estimation Using Artificial Intelligence Trial), which is not on this map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Obstetric Risk Assessment & Cesarean-delivery in Labor Estimation Using Artificial Intelligence Trial (ORACLE-AI)
Who cites it
4 citing papers in PubMed, 15 citations in OpenAlex.
- Comparison of an Ultrasound-Based System and the Bishop Score for Predicting Labor Induction Timing in Singleton Pregnancies at Term: A Prospective Cohort Study.International journal of women's health · 2026Article
- Use of the WHO partograph and Zhang's guideline for labor and delivery in China: implications for clinical practice.BMC pregnancy and childbirth · 2024Article
- New labor curves of dilation and station to improve the accuracy of predicting labor progress.American journal of obstetrics and gynecology · 2024Article
- Labor patterns of spontaneous first-stage labor in Chinese women with normal neonatal outcomes.PloS one · 2024Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
The assessment of labor progress is germane to every woman in labor. Two labor disorders-arrest of dilation and arrest of descent-are the primary indications for surgery in close to 50% of all intrapartum cesarean deliveries and are often contributing indications for cesarean deliveries for fetal heart rate abnormalities. Beginning in 1954, the assessment of labor progress was transformed by Friedman. He published a series of seminal works describing the relationship between cervical dilation, station of the presenting part, and time. He proposed nomenclature for the classification of labor disorders. Generations of obstetricians used this terminology and normal labor curves to determine expected rates of dilation and fetal descent and to decide when intervention was required. The analysis of labor progress presents many mathematical challenges. Clinical measurements of dilation and station are imprecise and prone to variation, especially for inexperienced observers. Many interrelated factors influence how the cervix dilates and how the fetus descends. There is substantial variability in when data collection begins and in the frequency of examinations. Statistical methods to account for these issues have advanced considerably in recent decades. In parallel, there is growing recognition among clinicians of the limitations of using time alone to assess progress in cervical dilation in labor. There is wide variation in the patterns of dilation over time and most labors do not follow an average dilation curve. Reliable assessment of labor progression is important because uncertainty leads to both over-use and under-use of cesarean delivery and neither of these extremes are desirable. This review traces the evolution of labor curves, describes how limitations are being addressed to reduce uncertainty and to improve the assessment of labor progression using modern statistical techniques and multi-dimensional data, and discusses the implications for obstetrical practice.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.