{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01752787,0.0005991576,0.0006173163,0.001143632,0.0004204409,0.00130911,0.0008563714,0.001118613,0.001914151],"category_scores_gemma":[0.05095236,0.0002082191,0.0008012093,0.0008699304,0.0009036551,0.002470613,0.001020097,0.002498037,0.0004412498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009204526,"about_ca_system_score_gemma":0.001004422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004185325,"about_ca_topic_score_gemma":0.002419728,"domain_scores_codex":[0.9977577,0.001255014,0.0001463366,0.0003661621,0.0002705343,0.000204246],"domain_scores_gemma":[0.9528736,0.03979085,0.002470594,0.002409139,0.001564258,0.0008914662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.006675209,0.000952846,0.6133072,0.0002153449,0.001176717,0.0002519288,0.0004660649,0.1755167,0.00338194,0.006163306,0.005290816,0.1866018],"study_design_scores_gemma":[0.0001571642,0.001126716,0.1419278,0.0000945106,0.0003309868,0.0002733183,0.0003149302,0.8273464,0.003254731,0.02380879,0.001276718,0.0000879642],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9664438,0.00283246,0.02531945,0.00253104,0.0003117086,0.00003143867,0.0007672099,0.000173609,0.00158916],"genre_scores_gemma":[0.9974317,0.0002191088,0.001374161,0.00008223786,0.000151593,0.000009658254,0.0003686931,0.00001849857,0.0003443512],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01752787,"threshold_uncertainty_score":0.09269732,"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."}}