{"id":"W2093320308","doi":"10.1159/000098518","title":"Disability Evolution in Multiple Sclerosis: How to Deal with Missing Transition Times in the Markov Model?","year":2007,"lang":"en","type":"article","venue":"Neuroepidemiology","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Missing data; Imputation (statistics); Medicine; Markov chain; Data set; Multiple sclerosis; Statistics; Data mining; Computer science; Mathematics; Psychiatry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003525458,0.0001962871,0.0005066174,0.0001799885,0.0001068253,0.000009725994,0.0001524109,0.0001139808,0.000007411957],"category_scores_gemma":[0.002637405,0.0001260097,0.00007112462,0.0004987693,0.0003475994,0.00009881485,0.000045038,0.0005536051,0.000004824055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002871378,"about_ca_system_score_gemma":0.00005200377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005927524,"about_ca_topic_score_gemma":0.004655383,"domain_scores_codex":[0.9971294,0.0009040969,0.0004337833,0.0005278132,0.000284541,0.0007203363],"domain_scores_gemma":[0.9966677,0.002693999,0.00006369719,0.0003600155,0.00006940312,0.0001452162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002736888,0.0005092803,0.933951,0.00009770756,0.00001829096,0.00004292755,0.00168049,0.005564927,0.02403414,0.00007708719,0.0008101367,0.0304771],"study_design_scores_gemma":[0.001527968,0.0003718366,0.9495149,0.0001247125,0.00001229941,0.00001929705,0.0004687943,0.04749965,0.0001418439,0.0001209369,0.000095314,0.0001024366],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.931976,0.00008627753,0.01769958,0.0488608,0.00002252974,0.001042934,0.000007641782,0.00003913305,0.0002650859],"genre_scores_gemma":[0.9871755,0.00004970094,0.009851294,0.002691414,0.00008877756,0.0001014287,0.00001156148,0.00001815209,0.00001216945],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05519948,"threshold_uncertainty_score":0.5138525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1099348959207014,"score_gpt":0.3367780167913176,"score_spread":0.2268431208706161,"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."}}