{"id":"W4399773302","doi":"10.1101/2024.06.17.24308959","title":"Frontotemporal Dementia Subtyping using Machine Learning, Multivariate Statistics, and Neuroimaging","year":2024,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University; Douglas Mental Health University Institute","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Compute Canada; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada; Biogen; Eli Lilly and Company; Alnylam Pharmaceuticals; Novo Nordisk; Eisai; Alzheimer's Association","keywords":"Subtyping; Frontotemporal dementia; Neuroimaging; Multivariate statistics; Dementia; Multivariate analysis; Psychology; Computer science; Artificial intelligence; Cognitive psychology; Machine learning; Medicine; Neuroscience; Internal medicine","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":[0.00566771,0.0008119216,0.0009225348,0.004227621,0.0004419127,0.001037167,0.0004405764,0.000483289,0.0009298703],"category_scores_gemma":[0.01598044,0.0001620016,0.001354774,0.002242852,0.0006611248,0.0006854159,0.0007949743,0.0009171077,0.0001821454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005617614,"about_ca_system_score_gemma":0.0007736717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004805913,"about_ca_topic_score_gemma":0.003743336,"domain_scores_codex":[0.997251,0.001640434,0.0002509409,0.0004187352,0.0002987856,0.0001401826],"domain_scores_gemma":[0.9874954,0.00896096,0.00180388,0.0007976392,0.0005540553,0.0003879911],"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":[0.0007768656,0.0002623856,0.9204597,0.0001107054,0.0009637254,0.0003112645,0.0001460417,0.01566012,0.001689923,0.0009179151,0.001254747,0.05744657],"study_design_scores_gemma":[0.00003930778,0.0003594556,0.5358446,0.00005595181,0.0002801828,0.0005938171,0.0002290554,0.4537621,0.001348824,0.006809514,0.0006167561,0.000060509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9597681,0.0008390182,0.03672355,0.0004489134,0.00004659361,0.00007171512,0.001227522,0.0002581677,0.0006164177],"genre_scores_gemma":[0.9909656,0.00007940011,0.008198784,0.00002003418,0.00003519654,0.00003535836,0.000572316,0.00001383843,0.00007951036],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00566771,"threshold_uncertainty_score":0.0299741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06286376947988413,"score_gpt":0.307701798404219,"score_spread":0.2448380289243349,"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."}}