{"id":"W2809364847","doi":"10.1177/1352458518783667","title":"Predicting risk of secondary progression in multiple sclerosis: A nomogram","year":2018,"lang":"en","type":"article","venue":"Multiple Sclerosis Journal","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia Hospital","funders":"Neuroförbundet; Biogen","keywords":"Multiple sclerosis; Nomogram; Medicine; Oncology; Immunology","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":[0.01076197,0.001080534,0.001055537,0.003883197,0.000559008,0.001924258,0.000885437,0.0008244122,0.001301005],"category_scores_gemma":[0.02921556,0.0002587055,0.001340477,0.001415732,0.0006273617,0.001166051,0.0009617763,0.0009310255,0.0005716918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005714262,"about_ca_system_score_gemma":0.0009666834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003538036,"about_ca_topic_score_gemma":0.003079192,"domain_scores_codex":[0.9971678,0.001850509,0.0001594416,0.000322799,0.0003866265,0.0001129424],"domain_scores_gemma":[0.9822174,0.01276475,0.001975978,0.0008420371,0.001575509,0.0006242528],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008290509,0.000132909,0.8738459,0.0001966149,0.0006080559,0.000205837,0.0002030439,0.05249029,0.0004241976,0.001026256,0.004086311,0.06595159],"study_design_scores_gemma":[0.0002299924,0.001348393,0.4652003,0.0004065413,0.0008026344,0.001162993,0.0006109248,0.5160142,0.001200666,0.005609047,0.0072583,0.0001559796],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8457972,0.003429597,0.1370341,0.002071569,0.0002695577,0.0005256746,0.004882682,0.0007236618,0.005266014],"genre_scores_gemma":[0.9636444,0.0005111182,0.03350043,0.0000965147,0.00009294847,0.0001429636,0.001636789,0.00003497238,0.0003399609],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01076197,"threshold_uncertainty_score":0.05691534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1044172356897661,"score_gpt":0.3204240787279831,"score_spread":0.2160068430382169,"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."}}