Publications by authors named "L Cantarutti"

: Seasonal influenza annually puts a significant burden on the pediatric population, especially the youngest, causing severe illness and death. Additionally, associated healthcare costs cause a significant financial strain on healthcare systems. While vaccination is the most effective prevention method, its cost-effectiveness for healthy children remains unassessed.

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Introduction: In the Veneto Region of Italy, universal varicella vaccination (VV) started in 2007 with a two-dose schedule at 12-15 months and 5-6 years of age achieving 90 % coverage in 2019. The study aimed at evaluating the vaccine effectiveness (VE) in children using a primary-care database METHODS: This retrospective analysis used Pedianet, a comprehensive database of 73 family paediatricians in the Veneto Region. Incidence rates (IR) of varicella were evaluated in children aged <14 years enrolled since birth, between January 2004 to April 2022.

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The significant impact of acute respiratory tract infections on healthcare systems is well-documented, given their contribution to emergency department admissions, hospitalizations, and increased use of antibiotics and other medications. However, further research is needed to understand the burden of acute respiratory tract infections in pediatric community care to develop effective public health interventions and improve child health outcomes. Real-world data were retrieved from Pedianet, an Italian network of over 200 family pediatricians.

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Purpose: X-linked hypophosphatemia (XLH) is a rare multi-systemic disease characterized by low plasma phosphate levels. The aim of this study was to investigate the annual XLH prevalence and internally evaluate predictive algorithms' application performance for the early diagnosis of XLH.

Methods: The PediaNet database, containing data on more than 400,000 children aged up to 14 years, was used to identify a cohort of XLH patients, which were matched with up to 10 controls by date of birth and gender.

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Free-text information represents a valuable resource for epidemiological surveillance. Its unstructured nature, however, presents significant challenges in the extraction of meaningful information. This study presents a deep learning model for classifying otitis using pediatric medical records.

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