{"id":"W3148523720","doi":"10.1038/s41467-021-22265-2","title":"Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data","year":2021,"lang":"en","type":"article","venue":"Nature Communications","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":255,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"National Institute of Mental Health; Canadian Institutes of Health Research; EMD Serono; MedDay Pharmaceuticals; Eisai; University College London; Multiple Sclerosis Society of Canada; Genentech; National Multiple Sclerosis Society; International Progressive MS Alliance; Myelin Repair Foundation; IXICO; NIH Blueprint for Neuroscience Research; Multiple Sclerosis Trust; Multiple Sclerosis Society; European Committee for Treatment and Research in Multiple Sclerosis; American Academy of Neurology; F. Hoffmann-La Roche; Pfizer; Biogen; McDonnell Center for Systems Neuroscience; Celgene; National Institute for Health and Care Research; Medical Research Council; Teva Pharmaceutical Industries; Department of Health and Social Care; Engineering and Physical Sciences Research Council; Acorda Therapeutics; University College London Hospitals NHS Foundation Trust; National Institutes of Health; Rosetrees Trust; European Commission; Sanofi","keywords":"Multiple sclerosis; Medicine; White matter; Lesion; Clinical trial; Limiting; Hyperintensity; Pathology; Machine learning; Magnetic resonance imaging; Bioinformatics; Neuroscience; Psychology; Computer science; Radiology; Biology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004721045,0.0001497955,0.0002875245,0.0001240446,0.0008517664,0.0001022284,0.0006774404,0.0001723281,0.00005765609],"category_scores_gemma":[0.0037512,0.0001272874,0.0000524152,0.0005223966,0.0002236548,0.0002221753,0.003319866,0.001574364,0.000009441555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000740258,"about_ca_system_score_gemma":0.0001264428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001845394,"about_ca_topic_score_gemma":0.002041734,"domain_scores_codex":[0.9984673,0.0002959983,0.0002484989,0.0003960169,0.0003343285,0.0002579027],"domain_scores_gemma":[0.9960039,0.0008175122,0.00007050564,0.002563071,0.0004071919,0.0001378003],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000564882,0.000357792,0.7421564,0.0001931174,0.0004406194,0.00001300343,0.0008640292,0.00001518763,0.2383599,0.00021169,0.001427778,0.01590402],"study_design_scores_gemma":[0.00344779,0.00004573017,0.6112205,0.0008812398,0.0003820511,0.00008723714,0.002163019,0.2390365,0.005875447,0.00003353321,0.1363832,0.0004437052],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5289348,0.4174961,0.001844342,0.04457146,0.0004145509,0.00165712,0.0007060617,0.0006172778,0.003758339],"genre_scores_gemma":[0.8938689,0.03882409,0.06579181,0.0002095742,0.00005656344,0.00001289688,0.0009924888,0.00002938268,0.0002142575],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.378672,"threshold_uncertainty_score":0.6839914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2549690966373321,"score_gpt":0.4102156895641029,"score_spread":0.1552465929267707,"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."}}