{"id":"W4409245772","doi":"10.1101/2025.04.02.25325137","title":"Discovering Hidden Links: Harnessing Similarity Network Fusion to Reveal Common Clusters in Healthy Aging, Mild Cognitive Impairment, and Dementia","year":2025,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Addiction and Mental Health; Carleton University","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Dementia; Similarity (geometry); Cognitive impairment; Cognition; Cognitive aging; Computer science; Psychology; Artificial intelligence; Cognitive psychology; Medicine; Neuroscience; Disease; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002433573,0.0009778342,0.001111736,0.005931179,0.0007187356,0.001158291,0.0009370895,0.001208774,0.0006108473],"category_scores_gemma":[0.008296155,0.0002840891,0.001342766,0.002321809,0.0006234483,0.001373281,0.0018494,0.0008907026,0.0002413487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007638329,"about_ca_system_score_gemma":0.0005499384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005785996,"about_ca_topic_score_gemma":0.006359212,"domain_scores_codex":[0.998718,0.0005034189,0.00007724282,0.0004079872,0.0001519786,0.0001412787],"domain_scores_gemma":[0.9971827,0.001483834,0.0004500088,0.000308904,0.000347683,0.0002268001],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002171869,0.001492908,0.5014282,0.0006192912,0.002578515,0.001410807,0.001899948,0.1749349,0.01731354,0.00552206,0.007420609,0.2832073],"study_design_scores_gemma":[0.00003457923,0.0002646564,0.08958054,0.00004938441,0.0002601201,0.0002382595,0.000449052,0.8900307,0.001721385,0.01617444,0.001141808,0.00005514995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8947932,0.001868047,0.09900502,0.0006342552,0.00007957588,0.0001403235,0.001768787,0.0004700472,0.001240746],"genre_scores_gemma":[0.9852886,0.0001847544,0.01246188,0.00006173838,0.00005247239,0.00004225549,0.001682845,0.00001506718,0.0002104205],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005931179,"threshold_uncertainty_score":0.01287007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02978405626678303,"score_gpt":0.350651749309725,"score_spread":0.3208676930429419,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}