↓ Skip to main content

PLOS

Prediction of High-Grade Vesicoureteral Reflux after Pediatric Urinary Tract Infection: External Validation Study of Procalcitonin-Based Decision Rule

Overview of attention for article published in PLOS ONE, December 2011
Altmetric Badge

Mentioned by

f1000
1 research highlight platform

Citations

dimensions_citation
15 Dimensions

Readers on

mendeley
35 Mendeley
Title
Prediction of High-Grade Vesicoureteral Reflux after Pediatric Urinary Tract Infection: External Validation Study of Procalcitonin-Based Decision Rule
Published in
PLOS ONE, December 2011
DOI 10.1371/journal.pone.0029556
Pubmed ID
Authors

Sandrine Leroy, François Bouissou, Anna Fernandez-Lopez, Metin K. Gurgoze, Kyriaki Karavanaki, Tim Ulinski, Silvia Bressan, Geogios Vaos, Pierre Leblond, Yvon Coulais, Carlos Luaces Cubells, A. Denizmen Aygun, Constantinos J. Stefanidis, Albert Bensman, Liviana DaDalt, Stefanos Gardikis, Sandra Bigot, Dominique Gendrel, Gérard Bréart, Martin Chalumeau

Abstract

Predicting vesico-ureteral reflux (VUR) ≥3 at the time of the first urinary tract infection (UTI) would make it possible to restrict cystography to high-risk children. We previously derived the following clinical decision rule for that purpose: cystography should be performed in cases with ureteral dilation and a serum procalcitonin level ≥0.17 ng/mL, or without ureteral dilatation when the serum procalcitonin level ≥0.63 ng/mL. The rule yielded a 86% sensitivity with a 46% specificity. We aimed to test its reproducibility.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 3%
Brazil 1 3%
Unknown 33 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 26%
Other 7 20%
Student > Bachelor 2 6%
Librarian 2 6%
Student > Postgraduate 2 6%
Other 6 17%
Unknown 7 20%
Readers by discipline Count As %
Medicine and Dentistry 16 46%
Agricultural and Biological Sciences 5 14%
Computer Science 1 3%
Nursing and Health Professions 1 3%
Immunology and Microbiology 1 3%
Other 1 3%
Unknown 10 29%