Protocol for a Systematic Review: Quantitative Assessment Methods and Biomarkers in Progressive Ataxias

This registered protocol outlines the methodology for a systematic review evaluating quantitative assessment tools and biomarkers in progressive ataxias. Progressive ataxias are a heterogeneous group of neurodegenerative disorders characterized by cerebellar dysfunction and progressive motor impairment. Early and objective quantification of disease progression remains a clinical challenge, and validated biomarkers are urgently needed for clinical trials and personalized management. The review will address the following research question using the PECO framework: • Population: Adults and children diagnosed with progressive ataxias (e.g., spinocerebellar ataxias, Friedreich's ataxia, multiple system atrophy–cerebellar type) • Exposure: Application of quantitative assessment methods (e.g., wearable sensors, motion capture, digital biomarkers, neuroimaging metrics) and/or molecular/cellular biomarkers • Comparison: Conventional clinical scales (e.g., SARA, BARS) or absence of quantitative assessment • Outcomes: (1) Psychometric properties (validity, reliability, responsiveness); (2) Correlation with disease severity/progression; (3) Feasibility and clinical utility Eligible study designs include observational studies (cross-sectional, longitudinal), validation studies, and clinical trials reporting quantitative outcomes. No language restrictions will be applied. The literature search will be conducted across PubMed/MEDLINE, Scopus, Web of Science, and EMBASE from inception to February 2026. Data extraction will include study characteristics, ataxia subtype, assessment methods, biomarker types, and outcome metrics. Risk of bias will be assessed using appropriate tools (e.g., QUADAS-2 for diagnostic accuracy studies, NIH Quality Assessment Tool for observational studies). Given expected methodological heterogeneity across studies, a narrative synthesis will be performed without meta-analysis. This protocol was registered prior to data extraction to ensure methodological transparency and minimize reporting bias.