{"id":"W4386871681","doi":"10.1093/eurheartj/ehab724.3161","title":"Temporal shift and accuracy of machine learning in heart transplant outcomes","year":2021,"lang":"en","type":"article","venue":"European Heart Journal","topic":"Transplantation: Methods and Outcomes","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Libin Cardiovascular Institute of Alberta","funders":"Vlaamse regering; Fonds Wetenschappelijk Onderzoek","keywords":"Medicine; Logistic regression; Random forest; Receiver operating characteristic; Hyperparameter; Heart transplantation; Machine learning; Artificial intelligence; Transplantation; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01442072,0.0005223841,0.0006092506,0.001450405,0.0003345413,0.0013672,0.0007209356,0.0007580731,0.001321851],"category_scores_gemma":[0.0391825,0.0002047705,0.0007323407,0.001058248,0.0006333843,0.001434673,0.0009553255,0.001247662,0.00044802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008735158,"about_ca_system_score_gemma":0.0007053609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004938578,"about_ca_topic_score_gemma":0.003098074,"domain_scores_codex":[0.9962108,0.001904208,0.0003592963,0.0008142253,0.0004213166,0.0002901322],"domain_scores_gemma":[0.969629,0.02272332,0.003218614,0.001922372,0.001908109,0.0005985831],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001265102,0.0002361779,0.7808829,0.0001374366,0.0004695943,0.0001220652,0.000163201,0.1495935,0.0009813532,0.0007506618,0.002216197,0.06318185],"study_design_scores_gemma":[0.00003462383,0.0003484768,0.2003843,0.00008265249,0.0001021902,0.0002524552,0.0001307199,0.7912931,0.002081441,0.00421583,0.001030722,0.00004357192],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9740782,0.001841783,0.01995169,0.0008699898,0.0001147974,0.00002980276,0.001144241,0.0002666095,0.0017028],"genre_scores_gemma":[0.9977749,0.00009223649,0.001301369,0.00004897625,0.00003374371,0.00000825155,0.000578704,0.00001762844,0.0001441286],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01442072,"threshold_uncertainty_score":0.07626498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05634673410576253,"score_gpt":0.3528258195569055,"score_spread":0.2964790854511429,"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."}}