{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006396395,0.0001435259,0.0005710814,0.0005146508,0.0001280345,0.00008138122,0.0001533682,0.0001476187,0.0001889227],"category_scores_gemma":[0.002009564,0.0001001959,0.0001568424,0.0007806581,0.00007998703,0.0001088671,0.0001076287,0.001664302,0.000001120096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001927822,"about_ca_system_score_gemma":0.0009565469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001747557,"about_ca_topic_score_gemma":0.0001058463,"domain_scores_codex":[0.9949881,0.001038283,0.0006624663,0.0002819996,0.00273638,0.0002927621],"domain_scores_gemma":[0.9976814,0.0005214828,0.0001850493,0.0001284941,0.00073853,0.0007449848],"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":[0.0004802281,0.0007360463,0.9801084,0.0002138142,0.004668757,0.002303271,0.003262237,0.0007949597,0.001194012,0.00001680916,0.0000472247,0.006174205],"study_design_scores_gemma":[0.006113946,0.001061614,0.1060568,0.001680841,0.002158568,0.00120007,0.0038186,0.8713934,0.005443643,0.00003358933,0.0008798936,0.0001590313],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9305966,0.0008912535,0.06669558,0.001316002,0.000147156,0.0001911136,0.0000068166,0.00001287218,0.0001425457],"genre_scores_gemma":[0.9638308,0.0004598403,0.03357932,0.00007531117,0.0001530394,0.000005156944,0.00003153794,0.0000181432,0.001846806],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8740516,"threshold_uncertainty_score":0.7230655,"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."}}