Dharma: A novel, clinically grounded machine learning framework for pediatric appendicitis-Diagnosis, severity assessment and evidence-based clinical decision support. [PDF]
Thapa Kshetri A +3 more
europepmc +1 more source
Differences in microbiota profiles of simple and complicated pediatric appendicitis cases. [PDF]
Öksüz L +5 more
europepmc +1 more source
CLR (CRP to Lymphocytes) Score for Differentiating Simple and Complicated Appendicitis in Pediatric Patients. [PDF]
Alper A +4 more
europepmc +1 more source
Neutrophil-to-lymphocyte ratio as an adjunct to clinical scoring systems for diagnosing perforated appendicitis in children: prospective cohort study. [PDF]
Saadat A +6 more
europepmc +1 more source
Nutritional Status as a Risk Factor for Appendiceal Perforation in Pediatric Acute Appendicitis: Systematic Review. [PDF]
Borca CI +8 more
europepmc +1 more source
Accuracy of the Alvarado Score in Diagnosing Appendicitis Among Children Versus Adults. [PDF]
Mashhour Al Habahbeh S +9 more
europepmc +1 more source
Retrospective Development of an AI Model Combining Ultrasound and Clinical Data for Pediatric Appendicitis Differentiation. [PDF]
Tan R +9 more
europepmc +1 more source
Usefulness of Laboratory-Based Machine Learning for Detection and Severity Classification of Acute Appendicitis in a Resource-Limited Healthcare Setting. [PDF]
Martinez-Fierro ML +10 more
europepmc +1 more source
Diagnostic Performance of Interleukin-6 (IL-6) and Membrane Glycoprotein Cluster of Differentiation-64 (CD64) for Acute Appendicitis in Girls Presenting with Lower Abdominal Pain. [PDF]
Filo E +5 more
europepmc +1 more source
Research on the construction of an artificial intelligence model for the early diagnosis of pediatric acute appendicitis: a single-center retrospective study based on complete blood count. [PDF]
Qiu S, Zhou J, Liu G, Wang D, Kang Q.
europepmc +1 more source

