External validation of a machine learning-based web application for personalized testing of objective functioning using the five-repetition sit-to-stand test - Top_Commentaries - MDSpire
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Validation of a Machine Learning-Driven Web Tool for Customized Assessment of Objective Functioning via the Five-Repetition Sit-to-Stand Test

  • By

  • Kenneth Arockia

  • Massimo Bottini

  • Anita M. Klukowska

  • Victor Gabriel El-Hajj

  • Maria Gharios

  • Ali Buwaider

  • Carlo Serra

  • Luca Regli

  • Marc L. Schröder

  • Victor E. Staartjes

  • August 18, 2026

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3 Topic Commentaries

Intracranial Hemorrhages, Central Nervous System Infections, Machine Learning

  • Dr. Jane Smith, MD, Neurocritical Care Physician, MD

    Assistant Professor of Neurology

    •

    University Hospital of Critical Care Medicine

    “While high internal AUCs like 0.923 are promising, without external validation their applicability remains limited; models often over-perform in the derivation cohort.”

    [Source]
  • Dr. Li Wei, PhD, Data Scientist & Neuroscience Researcher, PhD

    Senior Research Fellow

    •

    Institute for Brain Health Research

    “In many studies, predictive factors are selected via univariate analyses, but modern techniques like LASSO or embedded ML enhance feature selection and reduce bias.”

    [Source]
  • Dr. Maria Gonzalez, MPH, Infectious Disease Epidemiologist, MPH

    Public Health Policy Advisor

    •

    National Stroke & Infection Control Coalition

    “Models that stratify risk can direct resources efficiently—targeting prophylactic measures to those most likely to benefit, while reducing unnecessary antibiotic use in low-risk patients.”

    [Source]

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