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This research article presents an analysis of risk stratification methods for febrile neutropenia in adolescent and young adult cancer patients, evaluating their effectiveness in predicting severe infection.
Year: 2023
Region / city: United Kingdom, Canada, Switzerland, The Netherlands, Germany
Subject: Risk Stratification in Medical Research
Document Type: Research Article
Organization / Institution: University of York, Leeds Teaching Hospitals, University of Leicester, The Hospital for Sick Children, University of Bern, University Medical Center Groningen, Johann Wolfgang Goethe University
Author(s): Robert S. Phillips, Kaljit Bhuller, Lillian Sung, Roland A. Ammann, Wim J. E. Tissing, Thomas Lehrnbecher, Lesley A Stewart
Target Audience: Medical Researchers, Oncologists, Pediatricians, Healthcare Professionals
Period of Validity: Not specified
Approval Date: Not specified
Date of Changes: Not specified
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Management of the Febrile Child
Year: 2024
Region / city: Canberra
Topic: Paediatric care, fever management
Document type: Clinical Practice Guideline
Organization / institution: Canberra Health Services
Author: Paediatric Improvement Collaborative
Target audience: Clinicians, Paediatric healthcare providers
Period of validity: Until review date 29/09/2025
Date of approval: 29/09/2025
Date of amendments: 29/09/2025
Antibiotics and cure rates in childhood febrile urinary tract infections: a systematic review and meta-analysis (1990–2016)
Year: 1990–2016
Region / City: International, multicenter
Topic: Pediatric febrile urinary tract infections, antibiotic efficacy
Document Type: Systematic review and meta-analysis
Institution / Organization: St George’s University of London; University of Athens; Institut National de la Santé et de la Recherche Médicale, Université Paris Diderot; University Children’s Hospital Basel; University of Pennsylvania School of Medicine
Authors: Konstantinos Vazouras, Romain Basmaci, Julia Bielicki, Laura Folgori, Theoklis Zaoutis, Mike Sharland, Yingfen Hsia
Study Population: Children aged 0–18 years with febrile urinary tract infections without major underlying conditions
Number of Studies Reviewed: 30 clinical trials
Total Cases Analyzed: 3,913
Antibiotics Evaluated: Cephalosporins, aminoglycosides, other antimicrobials
Key Outcomes: Clinical cure rates, microbiological cure rates, treatment failure rates
Data Sources: Medline, Embase, Cochrane Central
Inclusion Criteria: Randomized clinical trials reporting on clinical and/or microbiological efficacy of antibiotics in pediatric febrile UTIs
Exclusion Criteria: Lower urinary tract infections, patients with underlying conditions, long-term endpoints >1 month
Diagnostic test accuracy of a 2-transcript host RNA signature for discriminating bacterial vs viral infection in febrile children
Year: 2016
Region / city: UK, Spain, Netherlands, USA
Topic: Pediatric infectious diseases, diagnostics
Document type: Research article
Institution: Imperial College London, Hospital Clínico Universitario de Santiago, University of California San Diego, Emma Children’s Hospital, University of Oxford, University of Southampton, University of Amsterdam, University of Queensland
Authors: Jethro A Herberg, Myrsini Kaforou, Victoria J Wright, Hannah Shailes, Hariklia Eleftherohorinou, Clive J Hoggart, Miriam Cebey-Lopez, Michael J Carter, Victoria A Janes, Stuart Gormley, Chisato Shimizu, Adriana H Tremoulet, Anouk M Barendregt, Antonio Salas, John Kanegaye, Andrew J Pollard, Saul N Faust, Sanjay Patel, Taco Kuijpers, Federico Martinon-Torres, Jane C Burns, Lachlan JM Coin, Michael Levin
Study period: 2009–2013
Population: Febrile children
Sample size: 370 children
Method: Whole-blood RNA expression profiling
Validation: Discovery and validation cohorts, additional datasets with meningococcal and inflammatory diseases
Key outcomes: Sensitivity and specificity of 2-transcript RNA signature (FAM89A and IFI44L) for bacterial vs viral infection
Corresponding author: Prof. Michael Levin, Imperial College London, [email protected]
Date of revision: 4 July 2016
Development of imaging features to refine risk stratification in high-risk neuroblastoma
Note: Year
Contextual description: A research paper providing advanced imaging analysis methods for refining risk assessment in high-risk neuroblastoma patients.
AutoFRS: An Externally Validated, Annotation-Free Approach to Computational Preoperative Complication Risk Stratification in Pancreatic Surgery – Supplementary Material
Year: 2025
Type of document: Supplementary digital content
Topic: Computational preoperative risk stratification in pancreatic surgery
Methods: Radiomics, feature extraction, hierarchical clustering, machine learning models
Patient cohorts: Dresden (Development), Heidelberg (External Test)
Imaging modality: CT
Software/Tools: TotalSegmentator v1.5.6, MIRP v2.0.0
Number of extracted features: 654
Cluster analysis: Hierarchical agglomerative clustering
Comparison: AutoFRS vs traditional FRS risk stratification
Content sections: Patient selection, CT imaging characteristics, Totalsegmentator configuration, MIRP feature extraction, Feature clustering, Radiomics and clinical signatures, Evaluation of models, Web application, Glossary
Supplementary materialRisk Stratification System of Gastrointestinal Stromal Tumors under Endoscopic Ultrasound Elastography Based on Artificial Intelligence
Year: 2023
Region / city: Wuhan, China
Topic: Medical Imaging, Artificial Intelligence, Gastrointestinal Stromal Tumors
Document Type: Research Article
Organization / Institution: Renmin Hospital of Wuhan University
Authors: Chenxia Zhang, Wei Tan, Xun Li, Xiao Tao, Bing Xiao, Wei Zhou, Honggang Yu
Target Audience: Medical professionals, researchers in gastroenterology and artificial intelligence
Period of Application: Not specified
Date of Approval: Not specified
Date of Changes: Not specified
Keywords: Gastrointestinal Stromal Tumors, Endoscopic Ultrasound, Artificial Intelligence, Risk Stratification, Deep Learning
Methodology: Image Classification, Image Segmentation, Deep Convolutional Neural Network (DCNN), ResNet, Unet++
Evaluation Metrics: Accuracy, Intersection over Union (IoU), Precision, Recall, F1 score
Conclusion: The use of AI-based risk stratification and image analysis improves GIST diagnosis accuracy
References: Li J, Zhu Y, Dong Z, et al., Yao L, Zhang J, Liu J, et al., Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J
Supplementary Digital Content Table 2. Stratification of Age Adjusted-Incidence Rate by Histology
Year: 2020
Region / City: United States
Topic: Oncology, Hematology
Document Type: Research data
Organization / Institution: N/A
Author: N/A
Target Audience: Researchers, medical professionals
Period of Validity: N/A
Approval Date: N/A
Date of Changes: N/A
Supplementary materialXpert MTB/RIF® cycle threshold as a marker of TB disease severity; Implications for TB treatment stratification
Year: 2021
Region / City: N/A
Subject: Tuberculosis, Diagnostic Methods, Treatment Stratification
Document Type: Research Supplement
Institution: N/A
Authors: Daniel J. Grint, Jasvir Dhillon, Philip D. Butcher, Jack Adams, Tulika Munshi, Adam Witney, Kate Gould, Ken Laing, Christopher Cousins, Sean Wasserman, Katherine Fielding, Tom Harrison, Amina Jindani
Target Audience: Healthcare professionals, researchers, clinicians
Period of Effectiveness: N/A
Date of Approval: N/A
Date of Changes: N/A
Topics: Neutropenic Febrile stratification