{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005820786,0.0001929249,0.0003791418,0.0002106148,0.00009623505,0.0000976386,0.0001483658,0.00005474229,0.00003165886],"category_scores_gemma":[0.001127672,0.0001548656,0.00009033496,0.0003948906,0.000172277,0.0005962532,0.0001151137,0.0004095163,0.00001161751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00058327,"about_ca_system_score_gemma":0.0002380267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002530359,"about_ca_topic_score_gemma":0.0003762722,"domain_scores_codex":[0.9973195,0.0001767948,0.0007151135,0.0005355739,0.0009298555,0.0003232312],"domain_scores_gemma":[0.9988909,0.000277741,0.0001091914,0.0002928627,0.0002141237,0.0002151421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000372905,0.01700689,0.9532834,0.0006650936,0.00008606521,0.000004651096,0.0009452458,0.0001196231,0.0002079575,0.000003683076,0.00006171042,0.02724276],"study_design_scores_gemma":[0.003326633,0.001463023,0.7569503,0.001317892,0.000007084346,7.140331e-7,0.0005055151,0.2359672,0.00006695985,0.000002516377,0.0003221384,0.00007011076],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9941707,0.0004025012,0.0001474422,0.002035284,0.0001577409,0.002122764,0.0006746833,0.0002285688,0.00006034995],"genre_scores_gemma":[0.9979919,0.0001299459,0.0004430461,0.00007192515,0.0000902059,0.0002693307,0.0009232723,0.00003561653,0.00004478556],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2358475,"threshold_uncertainty_score":0.6315237,"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."}}