Assessing the applicability of adult functional prognostic stroke scores in pediatric population in resource limited settings.

troke in children is a rare but potentially devastating condition, with long-term functional, cognitive, and psy- chosocial consequences. Adult functional prognostic scores, including the National Institutes of Health Stroke Scale (NIHSS) and the modified Rankin Scale (mRS), are extensively utilized to forecast outcomes post-stroke; however, their relevance in pediatric populations is still ambiguous. Pediatric stroke differs from adult stroke in etiology, including sickle cell disease, congenital heart defects, arteriopathies, and infections, and recovery tra- jectories are influenced by neurodevelopmental plastic- ity. Resource-limited settings further complicate prog- nostication due to constrained access to neuroimaging, rehabilitation, and specialized care. This perspective ex- amines the limitations of applying adult functional prog- nostic scores to children, highlighting risks of misclassi- fication and bias. We propose the development of hybrid, pediatric-adapted prognostic tools that integrate clinical, developmental, and contextual determinants of recovery. Artificial intelligence (AI) presents significant prospects for improving prediction accuracy by integrating multi- dimensional data and simulating outcomes under diverse resource constraints. We emphasize ethical considera- tions, such as the equitable allocation of scarce resources and parental engagement. Developing validated, context- sensitive pediatric stroke prognostic scores could im- prove individualized care, optimize resource utilization, and support long-term functional recovery in children, particularly in low-resource environments.