{"id":"W3037795616","doi":"10.1093/brain/awaa162","title":"Multiple sclerosis lesions in motor tracts from brain to cervical cord: spatial distribution and correlation with disability","year":2020,"lang":"en","type":"article","venue":"Brain","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières; Montreal Neurological Institute and Hospital; Université de Montréal; Université du Québec à Montréal; Polytechnique Montréal","funders":"National Institute of Neurological Disorders and Stroke; Institut de Valorisation des Données; Fondation pour l'Aide à la Recherche sur la Sclérose en Plaques; National Institutes of Health; Vetenskapsrådet; Hjärnfonden","keywords":"Corticospinal tract; Spinal cord; Lesion; Medicine; Expanded Disability Status Scale; Pyramidal tracts; Multiple sclerosis; White matter; Central nervous system disease; Magnetic resonance imaging; Pathology; Anatomy; Diffusion MRI; Radiology; Surgery","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":[0.0002324728,0.0001660619,0.0003320327,0.00003173531,0.000103826,0.00002943234,0.00007091663,0.00009243297,0.00008972468],"category_scores_gemma":[0.006147854,0.000135498,0.00004340536,0.0003120562,0.0001699979,0.0001010499,0.0001232124,0.0002862834,0.00002722656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001636672,"about_ca_system_score_gemma":0.00005017857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002932596,"about_ca_topic_score_gemma":0.004811061,"domain_scores_codex":[0.9983829,0.0001619449,0.0002788777,0.0004807131,0.0004026624,0.0002928919],"domain_scores_gemma":[0.9980178,0.00122706,0.00004536987,0.0002039804,0.00006656753,0.0004392522],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004149864,0.0003926707,0.8693554,0.0000740073,0.00003835612,0.00001529925,0.001585909,0.00007219159,0.04297218,0.00001538438,0.004087815,0.07724085],"study_design_scores_gemma":[0.003413399,0.0007243328,0.9764258,0.0001525666,0.00001491616,0.000001117776,0.0003651876,0.01690695,0.0004494931,0.000009400332,0.00140092,0.0001359245],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.924206,0.00002711792,0.007592119,0.06638503,0.00002983849,0.001115555,0.000555584,0.00005833202,0.00003049081],"genre_scores_gemma":[0.9969237,0.00001374346,0.0009777329,0.001376249,0.0001691816,0.00008935683,0.0004121097,0.00001804822,0.00001990875],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1070703,"threshold_uncertainty_score":0.7360001,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07502208406369552,"score_gpt":0.302675742191108,"score_spread":0.2276536581274125,"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."}}