{"id":"W2082668791","doi":"10.1111/j.1432-2277.2005.00212.x","title":"Predicting mortality after kidney transplantation: a clinical tool","year":2005,"lang":"en","type":"article","venue":"Transplant International","topic":"Renal Transplantation Outcomes and Treatments","field":"Medicine","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University Health Network","funders":"Canadian Society of Transplantation; Canadian Society of Nephrology","keywords":"Medicine; Comorbidity; Dialysis; Proportional hazards model; Transplantation; Kidney transplantation; Survival analysis; Intensive care medicine; Disease; Kidney disease; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002239942,0.0002042415,0.0002768602,0.000102723,0.00005378408,0.00003351278,0.0001211997,0.0001172075,0.002026343],"category_scores_gemma":[0.00001541825,0.0001695627,0.0003012477,0.00007809817,0.00005708542,0.0002467009,0.00000448503,0.0002355515,0.0002459547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005721716,"about_ca_system_score_gemma":0.0001041647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003202852,"about_ca_topic_score_gemma":0.00004397634,"domain_scores_codex":[0.9981348,0.00004267408,0.0006944383,0.000354322,0.000545047,0.0002287429],"domain_scores_gemma":[0.999307,0.00009274724,0.00008262663,0.0001812743,0.0000911915,0.0002451441],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001402835,0.0004179641,0.9928397,0.0001441749,0.0006488712,0.0005990299,0.0003880243,0.00002438389,0.0001250445,0.00044855,0.0000794473,0.002881981],"study_design_scores_gemma":[0.005545289,0.0001258754,0.9842118,0.0005568211,0.0005813682,0.0008586177,0.000008793644,0.0013383,0.0008407432,0.00003804841,0.005700981,0.0001933721],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9777441,0.00005437238,0.00357268,0.002519975,0.0007122961,0.0003516608,0.0009885909,0.0001488035,0.01390753],"genre_scores_gemma":[0.9900598,0.0008908951,0.003095599,0.002766558,0.0007320471,0.00006059401,0.001107732,0.0000225063,0.001264294],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01264324,"threshold_uncertainty_score":0.9988859,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04100917692298799,"score_gpt":0.367898429394718,"score_spread":0.32688925247173,"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."}}