{"id":"W3099884563","doi":"10.18653/v1/2020.clinicalnlp-1.2","title":"Multiple Sclerosis Severity Classification From Clinical Text","year":2020,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Toronto Rehabilitation Institute; University of Toronto; Ontario Institute for Cancer Research","funders":"","keywords":"Natural history; Computer science; Natural language processing; Multiple sclerosis; Artificial intelligence; Medicine; Internal medicine; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001723117,0.0009197132,0.0009417767,0.009040609,0.0003003789,0.001447232,0.0006269526,0.001031376,0.005174016],"category_scores_gemma":[0.00984762,0.0002012477,0.0009110907,0.002520181,0.000211267,0.001180264,0.001231198,0.0009957108,0.006623806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004170439,"about_ca_system_score_gemma":0.0004568171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001933735,"about_ca_topic_score_gemma":0.003599342,"domain_scores_codex":[0.998728,0.0002948147,0.000233527,0.0003466668,0.0002671101,0.0001298598],"domain_scores_gemma":[0.9954615,0.002139205,0.0004950149,0.0004377716,0.001098884,0.0003676126],"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":[0.002894559,0.0006790966,0.2265615,0.001843809,0.00127911,0.001195046,0.0003013551,0.004454645,0.01261179,0.0007481283,0.1508778,0.5965531],"study_design_scores_gemma":[0.0007276211,0.001538663,0.68784,0.001979128,0.001537063,0.005775931,0.001724281,0.1776269,0.01698357,0.01377086,0.09020728,0.0002887572],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6804144,0.0222299,0.02476154,0.006401634,0.002350015,0.001145359,0.2422416,0.005411131,0.01504437],"genre_scores_gemma":[0.6862577,0.004224593,0.02778652,0.0005662356,0.002256015,0.0006462234,0.2739094,0.0001910196,0.00416221],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009040609,"threshold_uncertainty_score":0.01730877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2310380793134054,"score_gpt":0.3534443390747336,"score_spread":0.1224062597613282,"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."}}