{"id":"W2889392446","doi":"10.1007/s11682-018-9942-9","title":"MRI-based prediction of conversion from clinically isolated syndrome to clinically definite multiple sclerosis using SVM and lesion geometry","year":2018,"lang":"en","type":"article","venue":"Brain Imaging and Behavior","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":39,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa; Ottawa Hospital","funders":"Wellcome Trust","keywords":"Multiple sclerosis; Support vector machine; Lesion; Grey matter; Medicine; Magnetic resonance imaging; Placebo; Clinically isolated syndrome; Pattern recognition (psychology); Neuroradiology; Artificial intelligence; Radiology; Pathology; Neurology; Computer science; Immunology; White matter","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001125021,0.0005447912,0.0006518172,0.001527015,0.0002436221,0.000741265,0.0004195033,0.0007258817,0.001149591],"category_scores_gemma":[0.003598923,0.0001299302,0.0006418573,0.0004174815,0.0002394514,0.0004411049,0.0004827407,0.0004973481,0.0004378719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002509924,"about_ca_system_score_gemma":0.0002942033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003058344,"about_ca_topic_score_gemma":0.002524577,"domain_scores_codex":[0.999643,0.00009032449,0.00004967016,0.000111145,0.00004308876,0.00006277807],"domain_scores_gemma":[0.9980793,0.0008971244,0.0003502226,0.0001205887,0.0002981442,0.0002545886],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001141004,0.000200079,0.9607744,0.00004369762,0.0003005366,0.0002151849,0.00004615089,0.00598667,0.00219233,0.00009293379,0.001107726,0.02789912],"study_design_scores_gemma":[0.00004405118,0.0004991645,0.698037,0.00004539964,0.0003938148,0.001198136,0.0002468128,0.2966037,0.001642718,0.0007589033,0.0004912158,0.00003917888],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9943388,0.0003131925,0.003712971,0.0001342021,0.00004230531,0.00001729725,0.0008287439,0.000106644,0.0005057842],"genre_scores_gemma":[0.9981502,0.00005281933,0.0009082095,0.00001155651,0.00002146191,0.000005824609,0.0007312894,0.000004185238,0.0001144836],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003058344,"threshold_uncertainty_score":0.006081104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1219784610999683,"score_gpt":0.3580910876067741,"score_spread":0.2361126265068058,"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."}}