{"id":"W4405376357","doi":"10.7717/peerj.18490","title":"A machine learning approach for identifying anatomical biomarkers of early mild cognitive impairment","year":2024,"lang":"en","type":"article","venue":"PeerJ","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hotchkiss Brain Institute; University of Calgary","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Alberta Innovates; Eisai; Northern California Institute for Research and Education; DoD Alzheimer's Disease Neuroimaging Initiative; BioClinica; Biogen; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; Engineering and Physical Sciences Research Council; Universiti Sains Malaysia; Bristol-Myers Squibb; Eli Lilly and Company; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; National Institute on Aging; Universiti Teknologi Petronas; Alzheimer's Association","keywords":"Feature selection; Alzheimer's Disease Neuroimaging Initiative; Artificial intelligence; Neuroimaging; Receiver operating characteristic; Computer science; Machine learning; Dementia; Medicine; Pattern recognition (psychology); Disease; Pathology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007005218,0.0001543291,0.0002862174,0.0003252486,0.00007888706,0.0000647782,0.00007362793,0.00006745398,0.0002063765],"category_scores_gemma":[0.0001480278,0.0001298225,0.0002571279,0.0003467999,0.0001121442,0.00009714363,0.00007472763,0.0002625039,0.00002705966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006553329,"about_ca_system_score_gemma":0.0001174385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008638556,"about_ca_topic_score_gemma":0.000001414591,"domain_scores_codex":[0.9984277,0.00006658822,0.000274377,0.0003744671,0.0004834374,0.0003734393],"domain_scores_gemma":[0.9993007,0.0001976018,0.00004105936,0.00009319479,0.0002130989,0.0001543439],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.009440399,0.002853382,0.784135,0.01064573,0.006486763,0.0003476838,0.004824517,0.00000927509,0.05155265,0.001560602,0.004259019,0.1238849],"study_design_scores_gemma":[0.01773866,0.008523091,0.7032689,0.002855317,0.00267515,0.0002186256,0.00484921,0.1723189,0.08091527,0.000890084,0.004808135,0.0009386431],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9650689,0.002338762,0.02524852,0.0003498687,0.0001060122,0.001462927,0.00007813016,0.000126107,0.005220779],"genre_scores_gemma":[0.9949981,0.00005546712,0.001847754,0.00005183827,0.00008576888,0.0001883101,0.000245822,0.00003625725,0.002490708],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1723096,"threshold_uncertainty_score":0.5294006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04448595203341586,"score_gpt":0.363280635971392,"score_spread":0.3187946839379761,"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."}}