María Teresa Murcia

ORCID: 0000-0003-4156-9124
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About
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Research Areas
  • Pregnancy and preeclampsia studies
  • Birth, Development, and Health
  • Gestational Diabetes Research and Management
  • Cervical Cancer and HPV Research
  • Genital Health and Disease

Universitat Autònoma de Barcelona
2021-2022

Vall d'Hebron Hospital Universitari
2021

Pre-eclampsia (PE) and small for gestational age (SGA) can be predicted from the first trimester. The most widely used algorithm worldwide is Fetal Medicine Foundation (FMF) algorithm. recently described Gaussian has reported excellent results although it unlikely to externally validated. Therefore, as an alternative approach, we compared predictive accuracy PE SGA of FMF algorithms.Secondary analysis a prospective cohort study was conducted at Vall d'Hebron University Hospital (Barcelona)...

10.1002/ijgo.14306 article EN cc-by International Journal of Gynecology & Obstetrics 2022-06-13

Objective The aim was to evaluate the cytology, colposcopic, and pathological factors associated with absence of high-grade squamous intraepithelial lesion (HSIL)/cervical neoplasia (CIN) 2–3 on loop electrosurgical excision procedure (LEEP) specimens in women cytology and/or HSIL/CIN biopsy risk disease persistence/recurrence. Materials Methods Two-center retrospective study undergoing LEEP for cervical between January 2014 December 2019. Clinical, colposcopy, pathology results were...

10.1097/lgt.0000000000000635 article EN Journal of Lower Genital Tract Disease 2021-12-20

Objective: To compare the predictive accuracy of Gaussian and FMF algorithms for preeclampsia (PE) small gestational age fetuses (SGA). Design: Secondary analysis a prospective cohort study. Setting: Tertiary referral hospital. Population: 2641 singleton pregnancies attending routine first-trimester scan from October 2015 to September 2017. Methods: Maternal characteristics, mean arterial blood pressure (MAP), uterine artery pulsatility index (UtAPI) were recorded at scan. Serum placental...

10.22541/au.170670232.26042184/v1 preprint EN Authorea (Authorea) 2024-01-31
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