{"id":"W4400974962","doi":"10.1371/journal.pdig.0000533","title":"Machine-learning-based prediction of disability progression in multiple sclerosis: An observational, international, multi-center study","year":2024,"lang":"en","type":"article","venue":"PLOS Digital Health","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre intégré de santé et de services sociaux de Chaudière-Appalaches; St. Michael's Hospital; Université de Montréal","funders":"Canadian Institutes of Health Research; Sanofi Genzyme; Novartis Pharma; EMD Serono; Fonds Wetenschappelijk Onderzoek; Vlaamse regering; Genesis Pharma; Teva Pharmaceutical Industries; Bristol-Myers Squibb; Fondazione Italiana Sclerosi Multipla; Biogen; Celgene; Eisai; Sanofi; Mylan; Università di Catania","keywords":"Brier score; Receiver operating characteristic; Observational study; Expanded Disability Status Scale; Machine learning; Artificial intelligence; Medicine; Computer science; Internal medicine; Multiple sclerosis","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":[0.005319686,0.0006267823,0.0006720357,0.00111911,0.0005282605,0.0009226631,0.0008428536,0.0007679723,0.001590432],"category_scores_gemma":[0.01107495,0.0002963225,0.0009264713,0.001652077,0.0004583946,0.001143124,0.001166856,0.001652366,0.0005698264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003510692,"about_ca_system_score_gemma":0.0005857478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002933841,"about_ca_topic_score_gemma":0.002124832,"domain_scores_codex":[0.9977837,0.000937258,0.0002685407,0.0005051761,0.0003105966,0.0001947772],"domain_scores_gemma":[0.9913708,0.002738284,0.002639359,0.001568492,0.0008356315,0.0008474739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008594056,0.000420143,0.9939349,0.00007898513,0.0003623827,0.00007229822,0.0001340163,0.0004184532,0.00008151484,0.00006098661,0.0008248956,0.002751919],"study_design_scores_gemma":[0.0001215097,0.001223046,0.9941049,0.00009183704,0.0002984985,0.000393971,0.0003390618,0.002399698,0.00009689773,0.0001515363,0.0007560417,0.00002300033],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962717,0.0004021711,0.0006607294,0.00006752222,0.00001014362,0.00006513849,0.002145702,0.00001385039,0.0003630134],"genre_scores_gemma":[0.992979,0.0002346257,0.000601012,0.00005381481,0.00003499429,0.00009871644,0.00583864,0.00001339628,0.0001456679],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005319686,"threshold_uncertainty_score":0.02813351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3144829305321339,"score_gpt":0.4140860895053005,"score_spread":0.09960315897316652,"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."}}