{"id":"W4307429571","doi":"10.1007/s13555-022-00827-6","title":"Predicting Clinical Remission of Chronic Urticaria Using Random Survival Forests: Machine Learning Applied to Real-World Data","year":2022,"lang":"en","type":"article","venue":"Dermatology and Therapy","topic":"Urticaria and Related Conditions","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Group for Research in Decision Analysis","funders":"Novartis Pharma; Genentech; AstraZeneca; Amgen; Pfizer; GlaxoSmithKline","keywords":"Random forest; Artificial intelligence; Machine learning; Medicine; Computer science","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":[0.000464603,0.0001084505,0.0004563317,0.0001310733,0.0003465711,0.000005537382,0.0001377034,0.00007700123,0.000170127],"category_scores_gemma":[0.0000699622,0.00008989045,0.00004952567,0.0002561724,0.0001489244,0.00003166445,0.0002067189,0.0005947399,0.000001715611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002458007,"about_ca_system_score_gemma":0.0001295155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001489057,"about_ca_topic_score_gemma":0.00009354948,"domain_scores_codex":[0.9986126,0.000286376,0.0004494424,0.0003008899,0.0001453708,0.0002052781],"domain_scores_gemma":[0.9989739,0.0003759705,0.0001374651,0.0003650276,0.00002605297,0.0001216058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003286884,0.0003112676,0.9758519,0.00001948115,0.001280228,0.0001081106,0.000392094,0.0004296383,0.008191193,0.00292224,0.0002724125,0.006934532],"study_design_scores_gemma":[0.05896207,0.002338373,0.3229069,0.0002353788,0.004024435,0.00272568,0.001190158,0.5508942,0.003086364,0.002871809,0.04989137,0.0008731953],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942653,0.00100449,0.001078962,0.001632021,0.0002616944,0.0003854627,0.00002481735,0.0000621878,0.001285132],"genre_scores_gemma":[0.9982625,0.0008170005,0.0002921467,0.0001888514,0.00009412832,0.00001459669,0.0002308623,0.00001933846,0.00008052742],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.652945,"threshold_uncertainty_score":0.3665626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06214066768005146,"score_gpt":0.3646165957382125,"score_spread":0.3024759280581611,"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."}}