{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003964632,0.0002879773,0.000246409,0.0002014567,0.0002605258,0.0003728189,0.0002056016,0.000106227,0.00009323363],"category_scores_gemma":[0.0006705382,0.0002954405,0.00005918528,0.0001378432,0.0001277357,0.00006996345,0.000749977,0.001289949,0.00005202679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006223176,"about_ca_system_score_gemma":0.00007299325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004550988,"about_ca_topic_score_gemma":0.00004649518,"domain_scores_codex":[0.9977586,0.0003873616,0.0003719694,0.0009357663,0.0002682312,0.0002780294],"domain_scores_gemma":[0.9991574,0.0001303355,0.0002672233,0.0002989484,0.00003696153,0.0001091115],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005692118,0.00008093457,0.1005893,0.0009227577,0.00007334874,0.0002900967,0.001278973,0.001385635,0.8793731,0.003878259,0.0001594334,0.01191124],"study_design_scores_gemma":[0.0002578626,0.00002236586,0.0248007,0.0001621587,0.0001318545,0.00005797555,0.00003083751,0.948786,0.01681718,0.005033135,0.003467559,0.0004323785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9591913,0.0009589155,0.03493176,0.0005889693,0.002794945,0.0003880422,0.0001220636,0.0004864828,0.0005375682],"genre_scores_gemma":[0.9958392,0.00009392691,0.003096267,0.0001804639,0.0001373914,0.00001391012,0.00001763566,0.00007235277,0.0005488252],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9474004,"threshold_uncertainty_score":0.9999498,"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."}}