{"id":"W2793279627","doi":"10.1371/journal.pone.0193523","title":"A novel learning algorithm to predict individual survival after liver transplantation for primary sclerosing cholangitis","year":2018,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Liver Diseases and Immunity","field":"Medicine","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Innovates; University of Alberta; University of Alberta Hospital; Alberta Hospital Edmonton","funders":"Health Resources and Services Administration; Minneapolis Medical Research Foundation; U.S. Department of Health and Human Services","keywords":"Liver transplantation; Medicine; Calculator; Calibration; Proportional hazards model; Transplantation; Discriminative model; Internal medicine; Algorithm; Statistics; Machine learning; Mathematics; 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":[0.000154694,0.0001087752,0.0001968243,0.00007993535,0.000128333,0.00003875936,0.00006508434,0.00005900547,0.0001358927],"category_scores_gemma":[0.00005479597,0.0001068231,0.00006277262,0.0001072942,0.00004454641,0.0001590564,0.00003249283,0.0001388982,0.00004493857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004864871,"about_ca_system_score_gemma":0.00005521292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007360618,"about_ca_topic_score_gemma":0.000005803574,"domain_scores_codex":[0.9990711,0.00002719148,0.0001451725,0.0001929804,0.0003369528,0.0002266034],"domain_scores_gemma":[0.9994472,0.0000600265,0.00003560037,0.0001319286,0.0001735295,0.0001517147],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.009222728,0.013672,0.3508408,0.005702614,0.004029745,0.0001307949,0.02446321,0.000004046841,0.3766719,0.00003477106,0.0006390588,0.2145883],"study_design_scores_gemma":[0.00227412,0.001248718,0.9854143,0.000799135,0.001061894,0.000009255237,0.00009340331,0.0005301135,0.008259497,0.000003974343,0.0001554852,0.0001500904],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9831193,0.0001299618,0.0149201,0.0001122044,0.00006581235,0.0006471405,0.0004591016,0.00008136372,0.0004649897],"genre_scores_gemma":[0.9559358,0.0001079519,0.04135749,0.0007019833,0.0008235431,0.0001054462,0.0005222593,0.00003733924,0.0004081588],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6345735,"threshold_uncertainty_score":0.4356118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05382532286857752,"score_gpt":0.2474855165501221,"score_spread":0.1936601936815446,"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."}}