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Elisenda Eixarch

Researcher at University of Barcelona

Publications -  188
Citations -  5280

Elisenda Eixarch is an academic researcher from University of Barcelona. The author has contributed to research in topics: Medicine & Gestational age. The author has an hindex of 32, co-authored 152 publications receiving 4297 citations. Previous affiliations of Elisenda Eixarch include Imperial College London & Hospital Sant Joan de Déu Barcelona.

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Fetoscopic laser surgery to decompress distal urethral obstruction caused by prolapsed ureterocele

TL;DR: It is demonstrated that fetoscopic decompression of a distal urethral obstruction is feasible in the rare event of congenital prolapsed ureterocele, and resolution of megacystis, reduction of hydronephrosis and normalization of amniotic fluid volume are observed.
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Longitudinal Assessment of Abdominal Circumference versus Estimated Fetal Weight in the Detection of Late Fetal Growth Restriction.

TL;DR: Longitudinal assessment of fetal growth during the third trimester has a low predictive capacity for late FGR, with no differences between conditional AC and conditional EFW.
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The prenatal management of neural tube defects: time for a re-appraisal

TL;DR: The fetal surgery centre in Leuven did not have a clinical programme for fetal NTD repair until the publication of the MOMS trial, so it allied to a high volume centre willing to share its expertise and assist with the first procedures.
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Leiomyomatosis peritonealis disseminata (2006: 9b).

TL;DR: A 35-year-old woman with regular periods and no history of pregnancy or use of oral contraceptive presented with a 3-week history of abdominal pain and a hypogastric mass, indicating leiomyomatosis peritonealis disseminata (LPD) without any evidence of malignancy.
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Learning and combining image neighborhoods using random forests for neonatal brain disease classification.

TL;DR: It is shown that combining multiple distances related to the condition improves the overall characterization and classification of the three clinical groups compared to the use of single distances and classical unsupervised manifold learning.