Results 191 to 200 of about 130,080 (303)
Hematologic markers and machine learning in predicting placenta accreta: A case–control study
Abstract Objective This study aims to enhance antenatal detection of placenta accreta spectrum (PAS) and predict severe hemorrhage at delivery using machine learning by evaluating the association between antenatal hematologic index trends across trimesters, imaging markers, and patient history.
Michael D. Jochum +11 more
wiley +1 more source
Expression of Cripto-1 protein in placentas from term pregnancies with and without fetal growth restriction: a retrospective cohort study in Croatia. [PDF]
Perković P +5 more
europepmc +1 more source
Abstract Objective To develop and internally validate a mechanistic, three‐domain framework for early classification and prediction of pre‐eclampsia (PE) using first‐trimester angiogenic, uteroplacental, and maternal vascular biomarkers. Methods In a prospective cohort of 1925 singleton pregnancies screened at 11 to 13.6 weeks, placental growth factor (
Johnatan Torres‐Torres +8 more
wiley +1 more source
The positive correlation between parity and overactive bladder in American adults: the mediating effect of body mass index. [PDF]
Zhao P +7 more
europepmc +1 more source
Obstetrical outcomes in pregnant patients following a gluten‐free diet: A prospective cohort study
Abstract Objective Concerns have been raised about the nutritional adequacy of a gluten‐free (GF) diet during pregnancy, specifically in the absence of celiac disease, and its impact on fetal development. The objective of this study was to investigate the association between a GF diet during pregnancy and obstetrical outcomes, with further sensitivity ...
Amelia Srajer +8 more
wiley +1 more source
Tali Eilon, B. Groner, I. Barash
semanticscholar +1 more source
Abstract Objective To evaluate trends in hysterectomy case volume for placenta accreta spectrum (PAS) disorder over time and compare maternal outcomes between high‐ and low‐volume centers. Specifically, we examined whether surgical volume influences severe maternal morbidity (SMM) and other key perioperative outcomes.
Lacey C. Brennan +9 more
wiley +1 more source
A comparative analysis of machine learning classifiers for modeling the number of liveborn piglets. [PDF]
Yang J +6 more
europepmc +1 more source

