{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004994831,0.0008544368,0.001061675,0.00390841,0.0004142703,0.001064592,0.0004428877,0.0004067285,0.0007689166],"category_scores_gemma":[0.01316334,0.0001953841,0.001442619,0.00231456,0.000581608,0.0006590161,0.0009532534,0.001000998,0.0002222086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006480622,"about_ca_system_score_gemma":0.001081164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006863235,"about_ca_topic_score_gemma":0.006239964,"domain_scores_codex":[0.997693,0.001301735,0.0002232162,0.0003388663,0.0003167167,0.0001264527],"domain_scores_gemma":[0.9941034,0.004055338,0.0007749156,0.0005125933,0.000369293,0.0001845724],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009639896,0.0004074501,0.6152416,0.000255679,0.001824942,0.000823995,0.0004761433,0.05716444,0.004432885,0.005211498,0.004406662,0.3087908],"study_design_scores_gemma":[0.00003562678,0.0002328812,0.2833977,0.00006768685,0.0002076047,0.0006539399,0.0002379766,0.6970652,0.001309291,0.01545929,0.001258234,0.00007450838],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7599334,0.001814909,0.2317159,0.0009841495,0.00008427544,0.000251076,0.002641136,0.001015034,0.001560172],"genre_scores_gemma":[0.9556376,0.0002389302,0.04238372,0.00004108178,0.00005846755,0.000105801,0.001297867,0.00003317115,0.0002033561],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006863235,"threshold_uncertainty_score":0.02641547,"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."}}