{"id":"W3107949646","doi":"10.1177/1352458520975323","title":"Accurate classification of secondary progression in multiple sclerosis using a decision tree","year":2020,"lang":"en","type":"article","venue":"Multiple Sclerosis Journal","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia Hospital","funders":"Canadian Institutes of Health Research","keywords":"Concordance; Medicine; Decision tree; Cohort; Clinical phenotype; Classifier (UML); Multiple sclerosis; Disease; Artificial intelligence; Phenotype; 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":[0.008756163,0.0007617166,0.001168374,0.003199364,0.0005525008,0.001448967,0.000821261,0.0009369659,0.0008333637],"category_scores_gemma":[0.01903928,0.0002561576,0.0009103184,0.001185972,0.0002998832,0.001091955,0.0005817394,0.0009130081,0.0005215878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00105664,"about_ca_system_score_gemma":0.001201072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004167736,"about_ca_topic_score_gemma":0.002675602,"domain_scores_codex":[0.9969131,0.001555682,0.0003389869,0.0003772375,0.0005895604,0.0002254932],"domain_scores_gemma":[0.9817148,0.01432993,0.0009291165,0.0004657775,0.002149771,0.0004105982],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002734137,0.0008567569,0.3014193,0.000334092,0.0007224408,0.0003972278,0.0003340387,0.2266693,0.002898152,0.001475168,0.006575255,0.4555841],"study_design_scores_gemma":[0.00009108749,0.0003985871,0.0210477,0.0000890091,0.0001732817,0.000206874,0.00007133697,0.9722864,0.001306582,0.003633783,0.0006673232,0.00002803214],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7542599,0.001416942,0.2381224,0.0009581412,0.0001840735,0.0003379333,0.001320545,0.001023786,0.002376253],"genre_scores_gemma":[0.9249359,0.000258875,0.07276317,0.0001325631,0.00006388673,0.000108713,0.001381873,0.00003343703,0.0003216115],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008756163,"threshold_uncertainty_score":0.04630756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3207078317502799,"score_gpt":0.367712893189055,"score_spread":0.04700506143877509,"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."}}