{"id":"W4411343702","doi":"10.3390/brainsci15060639","title":"Bridging the Gap: Missing Data Imputation Methods and Their Effect on Dementia Classification Performance","year":2025,"lang":"en","type":"article","venue":"Brain Sciences","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","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; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; National Institute on Aging; Alzheimer's Association","keywords":"Bridging (networking); Imputation (statistics); Missing data; Dementia; Computer science; Data mining; Psychology; Artificial intelligence; Machine learning; Medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1437883,0.001094876,0.00192853,0.002125278,0.001260851,0.002719874,0.001911095,0.002521418,0.001669391],"category_scores_gemma":[0.2927217,0.0005527631,0.002413417,0.002293505,0.001740234,0.00401843,0.002168173,0.003300904,0.0005836144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008960817,"about_ca_system_score_gemma":0.001607548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001943551,"about_ca_topic_score_gemma":0.001481489,"domain_scores_codex":[0.9243123,0.06222012,0.004001495,0.004435471,0.004258735,0.0007719136],"domain_scores_gemma":[0.4823923,0.4738053,0.01646018,0.01577658,0.01028035,0.001285129],"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":[0.01003032,0.0009297091,0.4634742,0.00146283,0.00565259,0.0004469656,0.002782891,0.09498511,0.003054664,0.004080556,0.005686637,0.4074135],"study_design_scores_gemma":[0.0006340159,0.006343404,0.2117335,0.002414788,0.003207956,0.001919752,0.001755146,0.7150143,0.018213,0.03186154,0.006417453,0.000485167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7141702,0.01422449,0.2613087,0.004852629,0.0004910963,0.0002715847,0.001117965,0.00107932,0.002484012],"genre_scores_gemma":[0.9306747,0.0008746143,0.06649883,0.0004589927,0.0001687501,0.0001782313,0.0006758402,0.0001462828,0.0003237274],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1437883,"threshold_uncertainty_score":0.7604345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1000815930345847,"score_gpt":0.4639385331354082,"score_spread":0.3638569401008235,"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."}}