{"id":"W4407347074","doi":"10.1093/braincomms/fcaf065","title":"Frontotemporal dementia subtyping using machine learning, multivariate statistics and neuroimaging","year":2024,"lang":"en","type":"article","venue":"Brain Communications","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University; Douglas Mental Health University Institute; Douglas College","funders":"Janssen Canada; Compute Canada; Canadian Institutes of Health Research; Janssen Biotech; Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Eisai Canada; Novo Nordisk; Biogen; LAM Therapeutics; Alnylam Pharmaceuticals; Eli Lilly and Company","keywords":"Subtyping; Frontotemporal dementia; Neuroimaging; Multivariate statistics; Computer science; Artificial intelligence; Multivariate analysis; Dementia; Psychology; Machine learning; Neuroscience; Medicine; Programming language","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":[0.0004471802,0.0001042337,0.0001256691,0.0001523885,0.0003419777,0.0001652596,0.0001462808,0.00002722255,0.0001504441],"category_scores_gemma":[0.0002927191,0.0001017081,0.00003408715,0.0001808602,0.0001518943,0.0001438578,0.0003621864,0.0004148031,0.00002404833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003745272,"about_ca_system_score_gemma":0.0001080757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000542108,"about_ca_topic_score_gemma":0.0000777438,"domain_scores_codex":[0.9990047,0.0002282846,0.00019735,0.0001950645,0.0001676221,0.0002069425],"domain_scores_gemma":[0.9989693,0.0003881745,0.00003402629,0.0004099138,0.00009678671,0.0001017579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000648701,0.0003465088,0.8654444,0.0004488832,0.000681015,0.0001280145,0.001700727,0.00001420693,0.03477148,0.01074559,0.002500402,0.08315396],"study_design_scores_gemma":[0.0008266997,0.0001172354,0.1783899,0.0002846326,0.0002786073,0.00009658677,0.0002126157,0.7371507,0.00011519,0.0007761962,0.08158281,0.0001688504],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5423041,0.05364962,0.3248745,0.05705632,0.0003793623,0.002336331,0.000286818,0.0009984404,0.01811453],"genre_scores_gemma":[0.9578348,0.000467616,0.03985416,0.0003709703,0.00003043721,0.00001641459,0.000205369,0.00002942997,0.001190736],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7371365,"threshold_uncertainty_score":0.4147538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06219335571656295,"score_gpt":0.3877557944166329,"score_spread":0.32556243870007,"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."}}