{"id":"W4414005941","doi":"10.1101/2025.09.03.25334761","title":"ci-fGBD: Cluster-Integrated Fast Generalized Bruhat Decomposition for Multimodal Data Clustering in Alzheimer's Disease.","year":2025,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Institute of Infection and Immunity","funders":"National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Indian Institute of Technology Delhi; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; Eli Lilly and Company; Foundation for the National Institutes of Health; BioClinica; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Eisai; Alzheimer's Association; Canadian Institutes of Health Research; National Science Foundation","keywords":"Cluster analysis; Cluster (spacecraft); Decomposition; Disease; Computer science; Artificial intelligence; Medicine; Chemistry; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.004535199,0.002222474,0.001374717,0.002354299,0.001214134,0.001901082,0.002045418,0.001753051,0.002688911],"category_scores_gemma":[0.01384174,0.0009354384,0.002389919,0.002158925,0.001004921,0.001636026,0.002647991,0.002972539,0.003198507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001220623,"about_ca_system_score_gemma":0.002622764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01404715,"about_ca_topic_score_gemma":0.02265011,"domain_scores_codex":[0.9979112,0.0009651247,0.0001009684,0.0004510824,0.0004037521,0.0001678705],"domain_scores_gemma":[0.9969163,0.001204857,0.0002579314,0.0007140865,0.0006792985,0.000227567],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001066949,0.0004159117,0.009577995,0.0009948858,0.001313059,0.0004643114,0.001206336,0.2283495,0.02439719,0.02534715,0.1156944,0.5911723],"study_design_scores_gemma":[0.00008831548,0.00008449848,0.003211839,0.00007941729,0.0000723144,0.0002483634,0.0001740374,0.9297636,0.004560114,0.04761888,0.01401383,0.00008489507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006615504,0.0008370807,0.9864498,0.0002988064,0.0001116589,0.000139465,0.001342006,0.003782158,0.000423641],"genre_scores_gemma":[0.07752809,0.0005774935,0.9098204,0.0003688995,0.0001273213,0.0004858614,0.007940465,0.001146062,0.002005341],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01404715,"threshold_uncertainty_score":0.02793074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06181391421268421,"score_gpt":0.3918715940080577,"score_spread":0.3300576797953734,"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."}}