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Cesarean Section

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Maternal and umbilical cord plasma purine concentrations after oral carbohydrate loading prior to elective Cesarean delivery under spinal anesthesia: a randomized controlled trial.

BMC pregnancy and childbirth
OBJECTIVE: To evaluate the effect of preoperative intake of oral carbohydrates versus standard preoperative fasting prior to elective cesarean delivery on plasma purine levels (hypoxanthine, xanthine, and uric acid) and beta-hydroxybutyrate (β-HB) in...

Machine learning model based on RCA-PDCA nursing methods and differentiating factors to predict hypotension during cesarean section surgery.

Computers in biology and medicine
BACKGROUND: Intraoperative hypotension during cesarean section has become a serious complication for maternal and fetal healthy. It is commonly encountered by subarachnoid anesthesia. However, currently used control methods have varying degrees of si...

Quantitative prediction of postpartum hemorrhage in cesarean section on machine learning.

BMC medical informatics and decision making
BACKGROUND: Cesarean section-induced postpartum hemorrhage (PPH) potentially causes anemia and hypovolemic shock in pregnant women. Hence, it is helpful for obstetricians and anesthesiologists to prepare pre-emptive prevention when predicting PPH occ...

Deep neural network (DNN) modelling for prediction of the mode of delivery.

European journal of obstetrics, gynecology, and reproductive biology
One of the factors that worry obstetricians the most is the method of delivery. In recent years, the rate of caesarean sections has steadily climbed and now exceeds the threshold advised by medical organizations. Obstetricians typically lack the tool...

Development of a machine learning approach for prediction of red blood cell transfusion in patients undergoing Cesarean section at a single institution.

Scientific reports
Despite recent advances in surgical techniques and perinatal management in obstetrics for reducing intraoperative bleeding, blood transfusion may occur during a cesarean section (CS). This study aims to identify machine learning models with an optima...

Machine Learning for the Prediction of Surgical Morbidity in Placenta Accreta Spectrum.

American journal of perinatology
OBJECTIVE:  We sought to create a machine learning (ML) model to identify variables that would aid in the prediction of surgical morbidity in cases of placenta accreta spectrum (PAS).

Predicting vaginal delivery after labor induction using machine learning: Development of a multivariable prediction model.

Acta obstetricia et gynecologica Scandinavica
INTRODUCTION: Induction of labor, often used for pregnancy termination, has globally rising rates, especially in high-income countries where pregnant women present with more comorbidities. Consequently, concerns on a potential rise in cesarean sectio...

Use of artificial intelligence to study the hospitalization of women undergoing caesarean section.

BMC public health
OBJECTIVE: The incidence of caesarean sections (CSs) has increased significantly in recent years, especially in developed countries. This study aimed to identify the factors that most influence the length of hospital stay (LOS) after a CS, using data...

Development and validation of machine learning models for predicting post-cesarean pain and individualized pain management strategies: a multicenter study.

BMC anesthesiology
BACKGROUND: Effective management of postoperative pain remains a significant challenge in obstetric care due to the variability in pain perception and response influenced by physical, medical, and psychosocial factors. Current standardized pain manag...