Analysis of Cesarean Section Rates in a Public Tertiary Hospital During Teaching and Non-teaching Periods Using the Robson Ten Group Classification System.

Introduction: The rising cesarean section (CS) rate is a global concern. One of the hospital characteristics that may explain the variation in CS among hospitals is hospital teaching status. This study aims to assess the rate of CS in a tertiary hospital during the teaching and non-teaching periods and to conduct an analysis using the Robson ten-group classification system.

Machine learning models for predicting pre-eclampsia: a systematic review protocol.

Introduction: Pre-eclampsia is one of the most serious clinical problems of pregnancy that contribute significantly to maternal mortality worldwide. This systematic review aims to identify and summarise the predictive factors of pre-eclampsia using machine learning models and evaluate the diagnostic accuracy of machine learning models in predicting pre-eclampsia.

Methods And Analysis: This review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

Machine Learning-Based Approach to Predict Intrauterine Growth Restriction.

Introduction: Creating a prediction model incorporating multiple risk factors for intrauterine growth restriction is vital. The current study employed a machine learning model to predict intrauterine growth restriction.

Methods: This cross-sectional study was carried out in a tertiary hospital in Bandar Abbas, Iran, from January 2020 to January 2022.