Computational Meta-Science Analysis of Dementia Research: Systematic Resource Misallocation and Strategic Reallocation Frameworks

Despite six decades of intensive research and over $50 billion in cumulative investment, dementia therapeutics demonstrate failure rates exceeding 99%, suggesting fundamental misalignment between research priorities and translational opportunities. We performed comprehensive computational meta-science analysis of 300,021 peer-reviewed articles spanning 1959–2025 using advanced machine learning approaches including Latent Dirichlet Allocation, validated through Multiple Correspondence Analysis, changepoint detection, and network topology analysis. Our analysis revealed systematic resource misallocation with 41.2% investment concentration in amyloid research yielding 0.4% therapeutic success rates, contrasted against 2.97% investment in voice biomarkers achieving 89.3% diagnostic accuracy. Three paradigm shifts emerged: symptom-based medicine (1959–1979), biomarker-centric approaches (1980–2004), and AI-driven precision medicine (2005–2025). Underinvested domains consistently demonstrate superior translational metrics across multiple disease categories, suggesting broader applicability beyond dementia. Strategic reallocation of research investment toward high-efficiency domains could potentially accelerate therapeutic breakthroughs by 3–4 years, contingent on complementary biological validation and regulatory adaptation. However, findings are bounded by corpus-based limitations and exclude unpublished trial failures and emerging translational technologies. This work establishes computational meta-science as a tool for optimizing biomedical research investment and calls for fundamental recalibration of research priorities grounded in translational relevance, biological plausibility, and evidence-based resource allocation.