{"id":"W4220847630","doi":"10.1016/j.healun.2022.03.019","title":"Temporal shift and predictive performance of machine learning for heart transplant outcomes","year":2022,"lang":"en","type":"article","venue":"The Journal of Heart and Lung Transplantation","topic":"Transplantation: Methods and Outcomes","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"Libin Cardiovascular Institute of Alberta; University of Calgary","funders":"Stanford Cardiovascular Institute, School of Medicine, Stanford University; KU Leuven; Health Resources and Services Administration; Fonds Wetenschappelijk Onderzoek; U.S. Department of Health and Human Services","keywords":"Logistic regression; Receiver operating characteristic; Confidence interval; Medicine; Random forest; Heart transplantation; Machine learning; Area under the curve; Transplantation; Internal medicine; Artificial intelligence; 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":[0.001112306,0.0001047189,0.000345719,0.0001157361,0.0002597206,0.000006538503,0.00004324568,0.00002790404,0.00001812295],"category_scores_gemma":[0.000008130194,0.00006740829,0.0000916516,0.00007370357,0.00005947781,0.0001166766,0.000003693617,0.0002883537,6.314561e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001258694,"about_ca_system_score_gemma":0.00004064312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003186929,"about_ca_topic_score_gemma":0.000008406324,"domain_scores_codex":[0.9989814,0.000175877,0.0003881667,0.00007806493,0.0002585882,0.0001179397],"domain_scores_gemma":[0.9992247,0.000469511,0.0001477566,0.00005096553,0.00004848866,0.00005854257],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.007202607,0.00005223649,0.9742729,0.0007915576,0.0001486968,0.000006734603,0.008872084,0.0007328027,0.007465032,0.00004141354,0.00002191245,0.0003920418],"study_design_scores_gemma":[0.003579538,0.002277632,0.9825569,0.0001585049,0.001046272,0.001760027,0.0004462881,0.004931743,0.00272583,0.00005182574,0.000380105,0.00008530371],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9899323,0.001220481,0.006234655,0.00208535,0.0001104617,0.0003039577,0.00009267239,0.00000823649,0.0000118908],"genre_scores_gemma":[0.9952722,0.001384881,0.003028232,0.0001747964,0.00003736345,0.000006805556,0.00004480123,0.00001185647,0.00003909968],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008425795,"threshold_uncertainty_score":0.274883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01606411891240192,"score_gpt":0.2977537630512142,"score_spread":0.2816896441388123,"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."}}