{"id":"W3034690129","doi":"10.1016/j.psychres.2020.113201","title":"Predicting Alzheimer's disease based on survival data and longitudinally measured performance on cognitive and functional scales","year":2020,"lang":"en","type":"article","venue":"Psychiatry Research","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Institute on Aging; Canadian Institutes of Health Research; National Natural Science Foundation of China; U.S. Department of Defense","keywords":"Akaike information criterion; Cognition; Receiver operating characteristic; Goodness of fit; Bayesian information criterion; Mini–Mental State Examination; Alzheimer's Disease Neuroimaging Initiative; Psychology; Medicine; Cognitive impairment; Statistics; Artificial intelligence; Internal medicine; Computer science; Psychiatry; Mathematics","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":[0.002745073,0.0006518777,0.0004854574,0.001832642,0.0003505415,0.0008782379,0.0004903897,0.000792085,0.001015296],"category_scores_gemma":[0.00678079,0.000213755,0.0006871868,0.0007280068,0.0002441983,0.0009580741,0.0005754862,0.0006337804,0.0003940148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002964533,"about_ca_system_score_gemma":0.0004560528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006589754,"about_ca_topic_score_gemma":0.009924469,"domain_scores_codex":[0.9995611,0.0001671208,0.00006720699,0.00006994489,0.00006783696,0.0000667694],"domain_scores_gemma":[0.9957803,0.00180007,0.0009426496,0.0002575994,0.0006303736,0.0005890501],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001795979,0.00005976918,0.9974028,0.000003776376,0.0000400872,0.00001530852,0.00002195443,0.000206617,0.00005359034,0.000009692354,0.00006762493,0.001939067],"study_design_scores_gemma":[0.0000238459,0.0004715627,0.9934695,0.000009081109,0.00009891167,0.0001679578,0.0001633175,0.005140821,0.0001295834,0.000168206,0.0001474186,0.000009735735],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986058,0.0001507414,0.0002619511,0.00004665633,0.00001152639,0.000008723621,0.0004409308,0.00001101266,0.0004627506],"genre_scores_gemma":[0.9984698,0.00008408783,0.0003568609,0.00001549166,0.00001499346,0.000007697484,0.000837513,0.000001439688,0.000212264],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006589754,"threshold_uncertainty_score":0.01451755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1675446142642864,"score_gpt":0.3894249369060108,"score_spread":0.2218803226417243,"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."}}