{"id":"W2994445848","doi":"10.1101/19011080","title":"Defining multiple sclerosis subtypes using machine learning","year":2019,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Engineering and Physical Sciences Research Council; McDonnell Center for Systems Neuroscience; National Institutes of Health; Università degli Studi di Genova; European Commission; Brigham and Women's Hospital; University College London; U.S. Department of Veterans Affairs; Università degli Studi di Siena; International Progressive MS Alliance; NIH Blueprint for Neuroscience Research; McGovern Medical School; McGill University; University of Texas Health Science Center at Houston; National Institute for Health and Care Research","keywords":"Multiple sclerosis; White matter; Medicine; Lesion; Magnetic resonance imaging; Clinical trial; Hyperintensity; Pathology; Internal medicine; Radiology; Psychiatry","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":[0.01004214,0.0008496248,0.001114122,0.003377417,0.0005301413,0.001984915,0.001003853,0.0009800004,0.001344651],"category_scores_gemma":[0.02209684,0.0002512376,0.00174413,0.001746069,0.0006501315,0.001006019,0.001263657,0.001201321,0.0004548998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006468084,"about_ca_system_score_gemma":0.0008060772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0027558,"about_ca_topic_score_gemma":0.001782506,"domain_scores_codex":[0.9944987,0.002601956,0.0007496605,0.001045493,0.0007106292,0.000393724],"domain_scores_gemma":[0.9801124,0.01276378,0.002827038,0.002222785,0.001554918,0.0005190403],"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":[0.001118393,0.0002945751,0.8957751,0.0001960002,0.001114046,0.0002179341,0.0001849697,0.02660131,0.001503662,0.000695426,0.002132475,0.07016607],"study_design_scores_gemma":[0.0002294287,0.0008840875,0.4162268,0.0003163461,0.000520301,0.0008245814,0.0005884542,0.5579113,0.002828377,0.01571247,0.003863971,0.00009395408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9651505,0.00146963,0.02748977,0.0004251206,0.0000729777,0.0003221024,0.003146373,0.0002293409,0.001694285],"genre_scores_gemma":[0.9897262,0.00008673458,0.006389872,0.00007862105,0.0000309622,0.00009714608,0.003308396,0.0000251652,0.0002570275],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01004214,"threshold_uncertainty_score":0.05310857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1198560266745727,"score_gpt":0.3291067831036777,"score_spread":0.2092507564291051,"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."}}