{"id":"W4293659886","doi":"10.3390/brainsci12091149","title":"Development of a Machine Learning Model to Discriminate Mild Cognitive Impairment Subjects from Normal Controls in Community Screening","year":2022,"lang":"en","type":"article","venue":"Brain Sciences","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Cohort; Neuropsychology; Receiver operating characteristic; Electroencephalography; Audiology; Neuropsychological assessment; Neuropsychological test; Cambridge Neuropsychological Test Automated Battery; Montreal Cognitive Assessment; Area under the curve; Psychology; Cognitive decline; Repeatable Battery for the Assessment of Neuropsychological Status; Cognition; Cognitive impairment; Medicine; Physical medicine and rehabilitation; Dementia; Internal medicine; Disease; Psychiatry; Working memory","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003599425,0.000139658,0.0002898802,0.0003985533,0.0008524183,0.00003390834,0.0002932686,0.00001938981,0.0002535164],"category_scores_gemma":[0.0003801809,0.0001227545,0.00005756884,0.0007154224,0.0002055345,0.0001282145,0.000660601,0.0005275374,0.000004849826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009656066,"about_ca_system_score_gemma":0.0003962424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001213277,"about_ca_topic_score_gemma":0.0006375855,"domain_scores_codex":[0.9973261,0.0005353303,0.0003626064,0.0002879899,0.001033998,0.0004539925],"domain_scores_gemma":[0.999032,0.000514823,0.00009814168,0.0001046121,0.00009028598,0.0001601788],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002828471,0.002086189,0.8370718,0.00009580527,0.0001366327,0.00006823799,0.0587288,0.004280659,0.04160238,0.00009235375,0.0001094761,0.05289914],"study_design_scores_gemma":[0.006665779,0.002959903,0.8238718,0.0004042717,0.00004447317,0.000009878057,0.0614846,0.08509602,0.01872252,0.0002136431,0.0002094383,0.0003176887],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9923649,0.00008857012,0.003861898,0.0009806844,0.00002266086,0.0007690688,0.00004672875,0.00002127846,0.001844152],"genre_scores_gemma":[0.9913873,0.000002458872,0.007066699,0.0009294804,0.000008822836,0.0001979598,0.00005376435,0.000008852219,0.0003446841],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08081536,"threshold_uncertainty_score":0.6556199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0693506548375756,"score_gpt":0.3553977692616819,"score_spread":0.2860471144241064,"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."}}