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Ureteropelvic junction obstruction (UPJO), characterised by prenatal or postnatal renal pelvis dilation, represents the primary cause of congenital paediatric hydronephrosis. UPJO may lead to impaired renal function in paediatric patients. Its pathogenesis includes genetic predisposition and anatomical abnormalities. While spontaneous resolution may occur in some infants, progressive hydronephrosis can lead to renal impairment without intervention. The assessment of the degree of hydronephrosis, renal dysfunction and surgical indications in paediatric patients before surgery is beneficial for providing doctors with surgical decisions. Pyeloplasty remains the gold-standard surgical intervention. Surgical approach selection, such as minimally invasive or open surgery and drainage method during surgery, directly affects outcomes. Many factors can affect postoperative complications and reoperation. Postoperative prognostic evaluation and renal function prediction remain key clinical focuses. Long-term follow-up data can provide significant clinical value. The application of neural network prediction models in this field still needs to be explored. This review aims to explore the update progress on risk prediction models of UPJO for children mainly over the past decade. We analysed various risk factors before, during and after surgery, intending to construct risk prediction models that cover the entire disease cycle in diagnosis and treatment. This review could provide a practical basis for surgeons to make clinical decisions.
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http://dx.doi.org/10.56434/j.arch.esp.urol.20257807.106 | DOI Listing |
Driven by eutrophication and global warming, the occurrence and frequency of harmful cyanobacteria blooms (CyanoHABs) are increasing worldwide, posing a serious threat to human health and biodiversity. Early warning enables precautional control measures of CyanoHABs within water bodies and in water works, and it becomes operational with high frequency in situ data (HFISD) of water quality and forecasting models by machine learning (ML). However, the acceptance of early warning systems by end-users relies significantly on the interpretability and generalizability of underlying models, and their operability.
View Article and Find Full Text PDFWater Res
September 2025
College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China. Electronic address:
Groundwater overextraction presents persistent challenges due to strategic interdependence among decentralized users. While game-theoretic models have advanced the analysis of individual incentives and collective outcomes, most frameworks assume fully rational agents and neglect the role of cognitive and social factors. This study proposes a coupled model that integrates opinion dynamics with a differential game of groundwater extraction, capturing the interaction between institutional authority and evolving stakeholder preferences.
View Article and Find Full Text PDFAm J Emerg Med
September 2025
University of Toronto, Rotman School of Management, Canada.
Study Objective: Accurately predicting which Emergency Department (ED) patients are at high risk of leaving without being seen (LWBS) could enable targeted interventions aimed at reducing LWBS rates. Machine Learning (ML) models that dynamically update these risk predictions as patients experience more time waiting were developed and validated, in order to improve the prediction accuracy and correctly identify more patients who LWBS.
Methods: The study was deemed quality improvement by the institutional review board, and collected all patient visits to the ED of a large academic medical campus over 24 months.
JMIR Res Protoc
September 2025
University of Nevada, Las Vegas, Las Vegas, NV, United States.
Background: In-hospital cardiac arrest (IHCA) remains a public health conundrum with high morbidity and mortality rates. While early identification of high-risk patients could enable preventive interventions and improve survival, evidence on the effectiveness of current prediction methods remains inconclusive. Limited research exists on patients' prearrest pathophysiological status and predictive and prognostic factors of IHCA, highlighting the need for a comprehensive synthesis of predictive methodologies.
View Article and Find Full Text PDFPurpose: In Armenia, a lower-middle-income country, cancer causes 21% of all deaths, with over half of cases diagnosed at advanced stages. Without universal health insurance, patients rely on out-of-pocket payments or black-market channels for costly immunotherapies, underscoring the need for real-world data to inform equitable policy reforms.
Methods: We conducted a multicenter, retrospective cohort study of patients who received at least one dose of an immune checkpoint inhibitor (ICI) between January 2017 and December 2023 across six Armenian oncology centers.