{"id":"W3092127333","doi":"10.14309/ajg.0000000000000971","title":"Low Predictability of Readmissions and Death Using Machine Learning in Cirrhosis","year":2020,"lang":"en","type":"article","venue":"The American Journal of Gastroenterology","topic":"Liver Disease and Transplantation","field":"Medicine","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Alberta","funders":"U.S. Department of Veterans Affairs","keywords":"Medicine; Predictability; Cirrhosis; Intensive care medicine; Internal medicine; Statistics","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":[0.003470822,0.0005454461,0.000412557,0.0009809779,0.0002468796,0.0009147989,0.0002609872,0.0003469763,0.0009650742],"category_scores_gemma":[0.01567243,0.0001565665,0.0004021119,0.0005671129,0.0003464715,0.0004593433,0.0005746355,0.0005652183,0.0002428957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003792589,"about_ca_system_score_gemma":0.0005753258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002096764,"about_ca_topic_score_gemma":0.002396923,"domain_scores_codex":[0.9986797,0.0007259073,0.0001125725,0.0001352624,0.0002087442,0.0001378103],"domain_scores_gemma":[0.9878776,0.007857312,0.002341077,0.000510279,0.0008746898,0.0005391766],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002003063,0.00002958519,0.9904826,0.00001614513,0.00006875464,0.00004979684,0.00001888745,0.002837069,0.0001426682,0.00002668683,0.0001304898,0.005997081],"study_design_scores_gemma":[0.00002224107,0.0004892005,0.9125506,0.0000376473,0.00008277634,0.0003803,0.00009357282,0.084528,0.0007321617,0.0007851241,0.0002810004,0.00001734691],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972735,0.0002944553,0.001637057,0.0001698064,0.00001451081,0.000008766457,0.0001755059,0.00003424719,0.0003922459],"genre_scores_gemma":[0.9994143,0.00004448144,0.0003010352,0.00001265174,0.00001174896,0.000003674723,0.0001609689,0.000002685953,0.00004836461],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003470822,"threshold_uncertainty_score":0.01835567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02048591284546742,"score_gpt":0.2690666491228403,"score_spread":0.2485807362773729,"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."}}