{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003468771,0.0008107708,0.0008141552,0.006634196,0.0003736946,0.001946237,0.0008343244,0.000733173,0.005330789],"category_scores_gemma":[0.03354619,0.0003000357,0.0004917678,0.003820698,0.0003345585,0.001651064,0.0008294616,0.0006658825,0.001089988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003651778,"about_ca_system_score_gemma":0.0009371301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002144384,"about_ca_topic_score_gemma":0.002267499,"domain_scores_codex":[0.9974101,0.001306763,0.0005420716,0.0002274479,0.0004355041,0.00007802285],"domain_scores_gemma":[0.9691921,0.02288687,0.003587372,0.0007329637,0.002653462,0.0009471615],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007166129,0.0004701901,0.763797,0.0002686028,0.0002171002,0.0002818059,0.0001586231,0.007650746,0.0004820044,0.001187229,0.02589604,0.198874],"study_design_scores_gemma":[0.0006256306,0.001595069,0.7643908,0.0006546493,0.0004702385,0.003345748,0.0009976367,0.1919201,0.002703764,0.01076918,0.02226856,0.0002586435],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7731059,0.003308815,0.1076625,0.01040162,0.0004355986,0.002805922,0.07092654,0.00701113,0.02434199],"genre_scores_gemma":[0.8726218,0.001036323,0.1089509,0.0004355151,0.0003043861,0.001261105,0.01426813,0.00008782889,0.00103412],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006634196,"threshold_uncertainty_score":0.01834482,"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."}}