Results 281 to 290 of about 3,070,666 (388)

FIGO good practice recommendations for vaginal birth after cesarean section

open access: yesInternational Journal of Gynecology &Obstetrics, EarlyView.
Abstract The rising global rate of cesarean section (CS) has prompted renewed focus on vaginal birth after cesarean (VBAC) as a safe and effective alternative to repeat CS in properly selected women. The FIGO good practice recommendations provide evidence‐based recommendations to guide VBAC care.
Eytan R. Barnea   +12 more
wiley   +1 more source

The impact of the COVID‐19 pandemic on the diagnosis and management of pre‐eclampsia: Identifying healthcare delays

open access: yesInternational Journal of Gynecology &Obstetrics, EarlyView.
Abstract Objective To compare the prevalence of pre‐eclampsia with and without severe features, and maternal and perinatal outcomes before and during the COVID‐19 pandemic. Methods Cross‐sectional study based on medical chart review of pregnant women admitted to a referral maternity hospital for childbirth between September 2019 to February 2020 ...
Juliana da‐Costa‐Santos   +12 more
wiley   +1 more source

Using artificial intelligence as a technological gynecologic and obstetric health: A narrative literature review

open access: yesInternational Journal of Gynecology &Obstetrics, EarlyView.
Abstract Maternal mortality remains a critical global public health issue, particularly in low‐ and middle‐income settings where failures in surveillance, early diagnosis, and clinical decision making compromise obstetric care. In this context, the present study aimed to critically review the scientific literature on the use of artificial intelligence (
Gustavo Gonçalves dos Santos
wiley   +1 more source

Long-term drought and risk of infant mortality in Africa: A cross-sectional study. [PDF]

open access: yesPLoS Med
Wang P   +7 more
europepmc   +1 more source

Establishment of a Machine Learning‐Based Prediction Model for Short‐Term Adverse Prognosis in Neonatal Bacterial Meningitis

open access: yesiLABMED, EarlyView.
This study systematically evaluated multiple machine learning models to predict short‐term adverse prognosis in neonatal bacterial meningitis using a retrospective cohort of 433 infants. The logistic regression model, combining mRMR‐selected and clinically prioritized variables, demonstrated optimal predictive performance (AUC = 0.908).
Ying Chen   +8 more
wiley   +1 more source

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