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Background: Percutaneous nephrolithotomy (PCNL) is the preferred treatment for large renal stones, yet variability in outcomes arises from patient-specific factors and institutional practices. Understanding complications and predictors of success is essential to improving procedural efficacy.

Objective: This study aimed to evaluate stone clearance rates, complications classified using the Clavien-Dindo system, and predictors of PCNL outcomes, with a focus on improving lower calyx stone clearance.

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Glycogen storage disease type Ia (GSDIa) is a rare inherited disorder resulting in potentially life-threatening hypoglycemia, metabolic abnormalities, and complications often requiring hospitalization. This retrospective database analysis assessed the complications, resource utilization, and costs in a large cohort of patients with GSDIa. We conducted a retrospective cohort study of GSDIa patients and matched non-GSDIa comparators utilizing the PharMetrics® Plus database.

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Background: The Multi-Specialty Working Group on the Recognition, Diagnosis, and Treatment of Primary Focal Hyperhidrosis developed evidence-based consensus criteria for diagnosing primary hyperhidrosis.

Objectives: To validate new questionnaire items for self-reported classification of primary hyperhidrosis based on the consensus criteria and to estimate the prevalence of primary hyperhidrosis.

Methods: This is a cross-sectional diagnostic accuracy study.

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Automated stenosis estimation of coronary angiographies using end-to-end learning.

Int J Cardiovasc Imaging

January 2025

Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.

The initial evaluation of stenosis during coronary angiography is typically performed by visual assessment. Visual assessment has limited accuracy compared to fractional flow reserve and quantitative coronary angiography, which are more time-consuming and costly. Applying deep learning might yield a faster and more accurate stenosis assessment.

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The paper introduces a method for predicting damage intensity in masonry residential buildings situated in mining areas, focusing on the impact of large-scale continuous ground deformation. The research utilizes in situ data collected in a database, encompassing structural and material features, as well as information on maintenance quality and building durability. In addition to this information, the database collected data on the intensity of continuous deformation of the mining area at the location of the building, as well as the range and intensity of damage identified in buildings.

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