{"id":"W4200228656","doi":"10.1101/2021.12.05.21267219","title":"Machine Learning Model for Predicting Outcomes of Biologic Therapy in Psoriasis","year":2021,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Psoriasis: Treatment and Pathogenesis","field":"Immunology and Microbiology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Ustekinumab; Medicine; Discontinuation; Adalimumab; Secukinumab; Psoriasis; Etanercept; Ixekizumab; Infliximab; Machine learning; Internal medicine; Artificial intelligence; Psoriatic arthritis; Computer science; Immunology; Disease; Tumor necrosis factor alpha","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":[0.003914084,0.0006752644,0.0008396528,0.001746138,0.0002545284,0.0008124932,0.0007587995,0.0007812559,0.001297284],"category_scores_gemma":[0.009879314,0.0002249464,0.0009095155,0.0009185037,0.0002794618,0.0004936153,0.0003817202,0.0008553842,0.0004102906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006522313,"about_ca_system_score_gemma":0.0007697142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005108836,"about_ca_topic_score_gemma":0.002171512,"domain_scores_codex":[0.9987674,0.0006749295,0.00009667098,0.000205652,0.0001550729,0.0001004192],"domain_scores_gemma":[0.9916629,0.007078211,0.0005011616,0.0002106843,0.0004512607,0.00009591324],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004033312,0.0002197408,0.07243513,0.00007102272,0.0002591049,0.0001613732,0.00004072615,0.8829317,0.0004282489,0.0007116557,0.00109516,0.04124284],"study_design_scores_gemma":[0.000009688623,0.00005599162,0.003898703,0.000008289261,0.00001562243,0.00003377008,0.000006876261,0.9949549,0.0001294784,0.0007844186,0.00009697604,0.000005372927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7253925,0.001558971,0.267143,0.0007291661,0.0001211418,0.0001344737,0.002490199,0.0009883138,0.001442252],"genre_scores_gemma":[0.984892,0.0001390816,0.01328558,0.00004405788,0.00004469577,0.00008654914,0.0009335877,0.00001363303,0.0005607866],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005108836,"threshold_uncertainty_score":0.02069992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04857536190293693,"score_gpt":0.2809537647892696,"score_spread":0.2323784028863327,"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."}}