{"id":"W4297500207","doi":"10.1136/jnnp-2022-329987","title":"Heterogeneity on long-term disability trajectories in patients with secondary progressive MS: a latent class analysis from Big MS Data network","year":2022,"lang":"en","type":"article","venue":"Journal of Neurology Neurosurgery & Psychiatry","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Intégré de Santé et de Services Sociaux des Laurentides; Université de Montréal","funders":"","keywords":"Latent class model; Term (time); Class (philosophy); Data science; Psychology; Computer science; Statistics; Mathematics; Artificial intelligence; Physics","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":[0.0204549,0.0006566435,0.001250714,0.002348882,0.001015613,0.002349116,0.001297258,0.001039314,0.002184718],"category_scores_gemma":[0.02609424,0.0003237811,0.003098755,0.002421055,0.001168053,0.001366122,0.002655505,0.001447309,0.0002996989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009472548,"about_ca_system_score_gemma":0.00110602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0110064,"about_ca_topic_score_gemma":0.006755249,"domain_scores_codex":[0.9905182,0.006680811,0.0004034953,0.001234252,0.0005167245,0.0006466631],"domain_scores_gemma":[0.9766356,0.01508793,0.003274922,0.003368883,0.0005878956,0.001044781],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008379369,0.00009862101,0.9820076,0.0000492183,0.001024754,0.0001527541,0.0003784151,0.007331949,0.0001822068,0.0005592357,0.0009780759,0.006399304],"study_design_scores_gemma":[0.0001217327,0.0002477639,0.8282411,0.0001002231,0.0006527816,0.0004203712,0.001051236,0.1633079,0.0001522462,0.004547057,0.001093995,0.0000636968],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915598,0.0003305816,0.004795487,0.0005448905,0.00002424315,0.00005518439,0.00238857,0.00004173377,0.000259605],"genre_scores_gemma":[0.9954516,0.0000707163,0.00120314,0.00003612855,0.00001728719,0.00005457356,0.003072753,0.00001226772,0.00008140926],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0204549,"threshold_uncertainty_score":0.1081771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04180952525871449,"score_gpt":0.3029829866338769,"score_spread":0.2611734613751624,"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."}}