{"id":"W4225751437","doi":"10.1177/13524585221084577","title":"Multiple Sclerosis Severity Score (MSSS) improves the accuracy of individualized prediction in MS","year":2022,"lang":"en","type":"article","venue":"Multiple Sclerosis Journal","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cegep de Saint Jerome; Centre intégré de santé et de services sociaux de Chaudière-Appalaches; Université de Montréal","funders":"National Health and Medical Research Council; Genentech","keywords":"Medicine; Proportional hazards model; Disease; Internal medicine; Metric (unit); Multiple sclerosis; Severity of illness; Prospective cohort study","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":[0.005155251,0.0009749848,0.001139316,0.00107592,0.0002987843,0.0009194026,0.0005233315,0.0004605049,0.001432711],"category_scores_gemma":[0.01366458,0.0002981548,0.0009542652,0.0006830117,0.0003090317,0.0007450376,0.001239411,0.000895369,0.0003985668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004494821,"about_ca_system_score_gemma":0.0009950512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004327079,"about_ca_topic_score_gemma":0.005349616,"domain_scores_codex":[0.9977393,0.001408087,0.0001172448,0.0003772065,0.0002750577,0.00008302282],"domain_scores_gemma":[0.9924426,0.004661113,0.00132576,0.0006349881,0.0006219066,0.0003135899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009377364,0.0001769584,0.8784112,0.0001301684,0.0009050636,0.00008558112,0.0001010943,0.04416522,0.0005098587,0.0002904159,0.001517555,0.07276929],"study_design_scores_gemma":[0.0001102403,0.001599111,0.4945879,0.0001601638,0.0008658984,0.000518396,0.0001014517,0.4951637,0.001379518,0.003294369,0.002142425,0.00007677015],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9674553,0.001541128,0.02659864,0.0007814911,0.0000756919,0.00006976895,0.001103552,0.000436366,0.001938037],"genre_scores_gemma":[0.9954944,0.0001381241,0.003781332,0.00003419969,0.00003099781,0.00001268876,0.0003017486,0.00001231472,0.0001942651],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005155251,"threshold_uncertainty_score":0.02726388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1285112502215492,"score_gpt":0.3045471859098928,"score_spread":0.1760359356883435,"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."}}