{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002044713,0.0007372642,0.0007176423,0.001394233,0.0003921248,0.0009833425,0.0008973146,0.0009197688,0.001012325],"category_scores_gemma":[0.003969315,0.0002136992,0.0007880545,0.0004039143,0.0002344651,0.0004610208,0.0005093553,0.000859129,0.0003548045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007655672,"about_ca_system_score_gemma":0.001046342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009078587,"about_ca_topic_score_gemma":0.005557673,"domain_scores_codex":[0.9994931,0.0001654884,0.00004375924,0.0001311798,0.00008669454,0.00007972156],"domain_scores_gemma":[0.9985695,0.0008736705,0.0001035021,0.00004654082,0.0003320669,0.00007469059],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009417927,0.00133257,0.2012668,0.0001484471,0.0005523977,0.0005544222,0.0002588526,0.5040486,0.004317604,0.001277703,0.005057628,0.2802432],"study_design_scores_gemma":[0.0000127874,0.00009499399,0.005203072,0.00001244236,0.00003118357,0.00004253989,0.00001673791,0.9937549,0.0003139306,0.0003551161,0.000154587,0.000007727859],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6691416,0.0008960646,0.3233245,0.001105408,0.0001673841,0.0003898683,0.0006819406,0.001256769,0.003036503],"genre_scores_gemma":[0.9731234,0.0001313478,0.0248532,0.0001218997,0.00004821354,0.0002201046,0.000559654,0.00001455407,0.0009275867],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009078587,"threshold_uncertainty_score":0.01805151,"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."}}