{"id":"W6939400359","doi":"10.60692/3axe6-wbp03","title":"Machine-learning-based prediction of disability progression in multiple sclerosis: an observational, international, multi-center study","year":2022,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre intégré de santé et de services sociaux de Chaudière-Appalaches; Université de Montréal","funders":"","keywords":"Brier score; Receiver operating characteristic; Calibration; Benchmark (surveying); Milestone; Quality of life (healthcare); Activities of daily living","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":[0.004500356,0.0005903747,0.0005974003,0.001108452,0.0004378544,0.0008876315,0.0007124012,0.0007765848,0.001032775],"category_scores_gemma":[0.008473268,0.0002422102,0.0008664019,0.001450993,0.0003972489,0.001101888,0.0007338813,0.001202663,0.0003121381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002993836,"about_ca_system_score_gemma":0.0004405878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002950364,"about_ca_topic_score_gemma":0.001697412,"domain_scores_codex":[0.9982288,0.0008917477,0.000188394,0.0003460591,0.0002106772,0.0001343094],"domain_scores_gemma":[0.9929311,0.002929602,0.002025153,0.0008220653,0.000609293,0.0006826528],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005170142,0.0002579282,0.9970246,0.00002745918,0.0001743225,0.00004297306,0.00006358625,0.0003270576,0.00005798689,0.00002380708,0.0001480229,0.00133523],"study_design_scores_gemma":[0.00006986589,0.0009741222,0.9942313,0.00003026239,0.0001651342,0.0002323827,0.0002333136,0.003673686,0.00008472078,0.00008320194,0.0002088053,0.0000130886],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986926,0.0002333095,0.0003303815,0.0000311087,0.000004020245,0.00002516776,0.0004969774,0.000005574536,0.000180991],"genre_scores_gemma":[0.9986554,0.00008218334,0.0001948074,0.00001529873,0.00001091882,0.00002711349,0.0009497608,0.000002628635,0.00006183961],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004500356,"threshold_uncertainty_score":0.02380043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2541454244301933,"score_gpt":0.3299706933980999,"score_spread":0.07582526896790664,"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."}}