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BACKGROUND: The diagnosis of failure to progress, the most common indication for intrapartum cesarean delivery, is based on the assessment of cervical dilation and station over time. Labor curves serve as references for expected changes in dilation and fetal descent. The labor curves of Friedman, Zhang et al, and others are based on time alone and derived from mothers with spontaneous labor onset. However, labor induction is now common, and clinicians also consider other factors when assessing labor progress. Labor curves that consider the use of labor induction and other factors that influence labor progress have the potential to be more accurate and closer to clinical decision-making. OBJECTIVE: This study aimed to compare the prediction errors of labor curves based on a single factor (time) or multiple clinically relevant factors using two modeling methods: mixed-effects regression, a standard statistical method, and Gaussian processes, a machine learning method. STUDY DESIGN: This was a longitudinal cohort study of changes in dilation and station based on data from 8022 births in nulliparous women with a live, singleton, vertex-presenting fetus ≥35 weeks of gestation with a vaginal delivery. New labor curves of dilation and station were generated with 10-fold cross-validation. External validation was performed using a geographically independent group. Model variables included time from the first examination in the 20 hours before delivery; dilation, effacement, and station recorded at the previous examination; cumulative contraction counts; and use of epidural anesthesia and labor induction. To assess model accuracy, differences between each models predicted value and its corresponding observed value were calculated. These prediction errors were summarized using mean absolute error and root mean squared error statistics. RESULTS: Dilation curves based on multiple parameters were more accurate than those derived from time alone. The mean absolute error of the multifactor methods was better (lower) than those of the single-factor methods (0.826 cm [95% confidence interval, 0.820-0.832] for the multifactor machine learning and 0.893 cm [95% confidence interval, 0.885-0.901] for the multifactor mixed-effects method and 2.122 cm [95% confidence interval, 2.108-2.136] for the single-factor methods; P

Using detailed patient-level data, we show that patients who enjoyed relevant medical training (physicians and midwives) are 3.1 percentage points less likely to receive a C-section than semi-expert patients (nurses and physiotherapists). We examined whether this treatment gap diminishes after a Dutch law reform that aimed to (1) strengthen patients' position in health care processes and (2) increase quality of care through more openness about clinical decisions and learning from medical incidents. We hypothesize that prescriptions of the law, such as more internal debates about appropriate treatment and improved communication of clinical decisions and potential complications during consultations, are expected to make semi-expert patients more informed. Exploiting the reform in a difference-in-differences approach, in which semi-expert patients constitute the treatment group and expert patients the control group, we show that the likelihood of semi-expert patients receiving a C-section after the reform is eight percentage points lower than the changes in C-section rates among expert patients. Given that the reform especially impacted the information level of semi-experts, these findings seem to support the idea that the treatment gap is driven, at least to some extent, by agency discrimination. [ABSTRACT FROM AUTHOR]