{"id":"W2963093468","doi":"10.1111/petr.13554","title":"Predicting ideal outcome after pediatric liver transplantation: An exploratory study using machine learning analyses to leverage Studies of Pediatric Liver Transplantation Data","year":2019,"lang":"en","type":"article","venue":"Pediatric Transplantation","topic":"Organ Transplantation Techniques and Outcomes","field":"Medicine","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"National Institute of Diabetes and Digestive and Kidney Diseases","keywords":"Medicine; Liver transplantation; Transplantation; Surgery; Internal medicine","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001188143,0.000728346,0.00111245,0.001576034,0.0002680282,0.00007144071,0.0004923118,0.0002714919,0.000202347],"category_scores_gemma":[0.00005611016,0.0006925588,0.0002395755,0.00164193,0.00004334558,0.001662746,0.00004510505,0.000658527,0.00006170251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001327045,"about_ca_system_score_gemma":0.0002317301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004926228,"about_ca_topic_score_gemma":0.0007939793,"domain_scores_codex":[0.994318,0.0005379376,0.001860791,0.001299114,0.001339098,0.0006450666],"domain_scores_gemma":[0.9972252,0.0005251133,0.0006378214,0.0008091681,0.0004368973,0.0003658608],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002151917,0.0005409792,0.9497035,0.02047815,0.000131094,0.001213337,0.02394958,0.001417025,0.000329753,0.000004811131,0.00000556288,0.00007429911],"study_design_scores_gemma":[0.006189938,0.001892352,0.962418,0.0001009226,0.02052178,0.0004059194,0.001954279,0.003759152,0.001804411,0.000007233626,0.000004362102,0.0009416578],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9818454,0.003923154,0.01006378,0.00003260712,0.0004631149,0.002313134,0.0009148999,0.0004145994,0.00002935988],"genre_scores_gemma":[0.8646187,0.1280213,0.004764907,0.0001180446,0.0009199894,0.00007491874,0.001313333,0.0001204864,0.00004834387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1240981,"threshold_uncertainty_score":0.9995525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1204019054463293,"score_gpt":0.3777948887319336,"score_spread":0.2573929832856043,"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."}}