{"id":"W2984612893","doi":"10.1177/2055217319885983","title":"Machine learning in secondary progressive multiple sclerosis: an improved predictive model for short-term disability progression","year":2019,"lang":"en","type":"article","venue":"Multiple Sclerosis Journal - Experimental Translational and Clinical","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Ottawa Hospital; University of British Columbia","funders":"Multiple Sclerosis Society of Canada","keywords":"Expanded Disability Status Scale; Logistic regression; Multiple sclerosis; Machine learning; Medicine; Receiver operating characteristic; Support vector machine; Clinical trial; Physical therapy; Artificial intelligence; Internal medicine; Computer science","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":[0.004205993,0.0006540009,0.0009699095,0.0009078882,0.0001776818,0.000763682,0.0005216385,0.0006489287,0.001053873],"category_scores_gemma":[0.00870579,0.0001408834,0.0007872686,0.000456587,0.0002025659,0.0005550068,0.000447101,0.001108241,0.0002691592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004701313,"about_ca_system_score_gemma":0.0007068649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002370784,"about_ca_topic_score_gemma":0.001887759,"domain_scores_codex":[0.9991868,0.0004718142,0.00004531601,0.0001265342,0.0001092192,0.00006032689],"domain_scores_gemma":[0.9959167,0.003123669,0.0003391182,0.0001757697,0.000337217,0.0001076438],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003193954,0.0008632753,0.1694357,0.0002497703,0.0008702701,0.0001924078,0.0001093959,0.5905226,0.002433052,0.001152489,0.003981962,0.2269951],"study_design_scores_gemma":[0.00008559084,0.0005458315,0.01350299,0.00003740024,0.0001192085,0.000074077,0.00001125001,0.9835169,0.0004706547,0.001244985,0.0003765532,0.00001467754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8843246,0.002759627,0.1081636,0.001342514,0.0001313874,0.0001722467,0.000862228,0.0005681766,0.001675694],"genre_scores_gemma":[0.987525,0.0003227942,0.01100977,0.0001121028,0.00007236815,0.00008129419,0.0004422216,0.00001573059,0.0004187517],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004205993,"threshold_uncertainty_score":0.02224368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1566684101755616,"score_gpt":0.4037198011592445,"score_spread":0.2470513909836829,"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."}}