{"id":"W3113178511","doi":"10.1002/alz.041676","title":"Machine‐learning‐based Alzheimer's disease dementia score using structural MRI neurodegeneration patterns: Independent validation on ADNI, AIBL, OASIS and MIRIAD","year":2020,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Neuroimaging; Dementia; Biomarker; Neurodegeneration; Magnetic resonance imaging; Artificial intelligence; Discriminative model; Alzheimer's Disease Neuroimaging Initiative; Imaging biomarker; Disease; Machine learning; Medicine; Computer science; Neuroscience; Psychology; Internal medicine; Radiology; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005942295,0.00153592,0.0009585264,0.002220391,0.0004645835,0.001094705,0.001387908,0.001134902,0.0007550778],"category_scores_gemma":[0.007417773,0.0002218583,0.0010122,0.0007343059,0.0004719997,0.0006234958,0.001081117,0.0008406667,0.0009370495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004853508,"about_ca_system_score_gemma":0.0006794507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007621845,"about_ca_topic_score_gemma":0.00646311,"domain_scores_codex":[0.9980926,0.0006702189,0.0001802348,0.0004788393,0.000438216,0.0001399247],"domain_scores_gemma":[0.9970577,0.0006834496,0.0002816226,0.0005062085,0.00109937,0.0003718375],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005553695,0.003263915,0.7112782,0.0005318904,0.003720646,0.0006030911,0.0002968779,0.05768129,0.01157613,0.000314167,0.01743483,0.1877454],"study_design_scores_gemma":[0.0005283633,0.002319828,0.4169525,0.000194676,0.0008489151,0.0009470595,0.0003696638,0.5610896,0.01271331,0.0008558605,0.003064509,0.000115746],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9881005,0.0008850667,0.005116902,0.0001742431,0.0001109801,0.0001962218,0.003865536,0.000580993,0.0009694865],"genre_scores_gemma":[0.9834164,0.0001880528,0.004596862,0.0000563201,0.00003773523,0.00008425832,0.01112611,0.00002408182,0.0004700726],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007621845,"threshold_uncertainty_score":0.03142625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05666571743236706,"score_gpt":0.3116311652260935,"score_spread":0.2549654477937264,"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."}}