{"id":"W3160457727","doi":"10.2196/26843","title":"Predicting Kidney Graft Survival Using Machine Learning Methods: Prediction Model Development and Feature Significance Analysis Study","year":2021,"lang":"en","type":"article","venue":"Journal of Medical Internet Research","topic":"Renal Transplantation Outcomes and Treatments","field":"Medicine","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Hennepin Healthcare Research Institute","keywords":"Kidney transplantation; Kidney; Kidney disease; Transplantation; Medicine; Feature (linguistics); Survival analysis; Computer science; Urology; Intensive care medicine; 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":[0.005489938,0.0008183862,0.0006544215,0.001074574,0.000222247,0.0007160525,0.000590018,0.0006170239,0.0005901173],"category_scores_gemma":[0.01192267,0.000206883,0.001210743,0.000761754,0.0002299237,0.0007527088,0.0005650365,0.001195727,0.0001629564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005778921,"about_ca_system_score_gemma":0.001122126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005094271,"about_ca_topic_score_gemma":0.00314931,"domain_scores_codex":[0.9988048,0.0005980228,0.00007125926,0.0002483322,0.0001883547,0.000089324],"domain_scores_gemma":[0.9935439,0.004686819,0.0003889947,0.0004816875,0.0007322813,0.0001663533],"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":[0.0005796977,0.00121233,0.3932213,0.0001524993,0.001231989,0.0003859701,0.0001739779,0.3637658,0.002060152,0.001418582,0.002555849,0.2332418],"study_design_scores_gemma":[0.00001493018,0.0001869036,0.01433926,0.00001532949,0.00006916944,0.00007081569,0.00002488751,0.9834394,0.0008438778,0.0007162998,0.0002671088,0.00001214813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8736876,0.00180266,0.1223007,0.0006395741,0.0000765143,0.0001223611,0.0004198099,0.000228076,0.0007227146],"genre_scores_gemma":[0.9685552,0.000451777,0.02992228,0.00006220894,0.00004871148,0.00007633674,0.0005360837,0.00002094779,0.000326548],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005489938,"threshold_uncertainty_score":0.02903396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1364481929897605,"score_gpt":0.4798393241112639,"score_spread":0.3433911311215034,"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."}}