{"id":"W2980352651","doi":"10.1016/j.jalz.2019.06.3870","title":"P4‐207: EMPLOYING ARTIFICIAL INTELLIGENCE IN THE DEVELOPMENT OF A SELF‐ADMINISTERED, COMPUTERISED COGNITIVE ASSESSMENT FOR THE ASSESSMENT OF NEURODEGENERATION","year":2019,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Montreal Cognitive Assessment; Receiver operating characteristic; Cognition; Dementia; Logistic regression; Cognitive Assessment System; Audiology; Cognitive impairment; Psychology; Cognitive test; Medicine; Psychiatry; Internal medicine; Disease","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":[0.002617327,0.0003237468,0.0003141719,0.000650419,0.0002348958,0.0006140188,0.0004396019,0.0004080052,0.001954955],"category_scores_gemma":[0.006069307,0.0001444517,0.000321511,0.0003669047,0.0004083778,0.0004525697,0.0004220669,0.0003824913,0.0007979475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002164825,"about_ca_system_score_gemma":0.001355189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001626837,"about_ca_topic_score_gemma":0.003663994,"domain_scores_codex":[0.9988604,0.0005998602,0.00008289501,0.0001256426,0.0002812165,0.00004999159],"domain_scores_gemma":[0.9974416,0.001555661,0.0001837783,0.000186644,0.0005039772,0.0001282168],"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":[0.003538968,0.004468771,0.3358728,0.0005538324,0.0002148492,0.0007951693,0.001124666,0.002894444,0.0355628,0.001800244,0.004917887,0.6082556],"study_design_scores_gemma":[0.0009464358,0.02812811,0.8515244,0.0002236309,0.0002910856,0.006062977,0.0005230804,0.04530859,0.0470328,0.003974332,0.01580915,0.0001755341],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9525841,0.0003567973,0.03549217,0.0003612525,0.00009057896,0.002697655,0.0006828497,0.000354566,0.007379952],"genre_scores_gemma":[0.8363007,0.0003149235,0.1567014,0.0004375441,0.00006880153,0.002783317,0.0009168639,0.00005049537,0.002425906],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002617327,"threshold_uncertainty_score":0.01384193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07674723386814347,"score_gpt":0.3942054184296355,"score_spread":0.3174581845614921,"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."}}