{"id":"W3136478203","doi":"10.1016/j.healun.2021.01.1836","title":"Predicting Cardiac Allograft Vasculopathy Profiles Using Machine Learning Clustering","year":2021,"lang":"en","type":"article","venue":"The Journal of Heart and Lung Transplantation","topic":"Transplantation: Methods and Outcomes","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University Health Network","funders":"","keywords":"Medicine; Internal medicine; Cardiology; Latent class model; Logistic regression; Cardiac allograft vasculopathy; Covariate; Confidence interval; Complication; Heart transplantation; Surgery; Transplantation; Machine learning","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":[0.001581808,0.0006007519,0.0008497067,0.003933921,0.0005465559,0.001807459,0.0008440317,0.0008993487,0.001062118],"category_scores_gemma":[0.004969158,0.0002133025,0.001002607,0.001792821,0.0002142864,0.0007232933,0.0006557616,0.0008087565,0.0007472866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005694,"about_ca_system_score_gemma":0.0006596769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003920838,"about_ca_topic_score_gemma":0.004251029,"domain_scores_codex":[0.99907,0.0002594698,0.00009875555,0.0002206115,0.0002029239,0.0001482667],"domain_scores_gemma":[0.9972595,0.001244226,0.0004146629,0.0003175598,0.0005594841,0.0002045623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001187874,0.001009472,0.7388682,0.0001088093,0.0005921582,0.0002946944,0.0001963026,0.04791895,0.005745049,0.001291261,0.004070783,0.1987164],"study_design_scores_gemma":[0.00004800736,0.0002825332,0.1388352,0.00005463301,0.000225155,0.0006665463,0.000329601,0.8493209,0.003935148,0.004735467,0.001505728,0.00006111259],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9029334,0.0007766594,0.09161618,0.0003510238,0.00007586592,0.0002079937,0.001907785,0.0005886337,0.001542302],"genre_scores_gemma":[0.9679819,0.000171426,0.02904973,0.00004061331,0.00004528306,0.00005548181,0.002072823,0.00003219983,0.0005506165],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003933921,"threshold_uncertainty_score":0.008365452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02059004217547605,"score_gpt":0.3032358577608944,"score_spread":0.2826458155854183,"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."}}