{"id":"W2118183539","doi":"10.1586/erm.10.119","title":"Identifying biomarkers as diagnostic tools in kidney transplantation","year":2011,"lang":"en","type":"article","venue":"Expert Review of Molecular Diagnostics","topic":"Renal Transplantation Outcomes and Treatments","field":"Medicine","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Institute of Diabetes and Digestive and Kidney Diseases","keywords":"Biomarker; Biomarker discovery; Medicine; Transplantation; Kidney transplantation; Intensive care medicine; Clinical Practice; Bioinformatics; Surrogate endpoint; Pathology; Internal medicine; Proteomics; Biology","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":[0.0001661107,0.0001967407,0.0004606953,0.0001372497,0.00002021992,0.000007833571,0.000101588,0.0000691621,0.0003105746],"category_scores_gemma":[0.002085272,0.0001669133,0.0002052908,0.0002864618,0.0000473505,0.0001004961,0.00001031401,0.00009211822,0.0000633009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003963373,"about_ca_system_score_gemma":0.00009674513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001622026,"about_ca_topic_score_gemma":0.000003930633,"domain_scores_codex":[0.9985559,0.0000890407,0.00058006,0.0002423857,0.0003303228,0.0002022716],"domain_scores_gemma":[0.9987922,0.0005276045,0.0001355834,0.0002718178,0.00008498832,0.0001878045],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"systematic_review","study_design_scores_codex":[0.001365921,0.007351259,0.4468599,0.2289621,0.003644636,0.05305423,0.01320411,0.000006267768,0.04023425,0.01405174,0.004362394,0.1869031],"study_design_scores_gemma":[0.009941683,0.001154302,0.2751568,0.3671217,0.002256765,0.001084546,0.0002111867,0.00001325295,0.3370011,0.0008705896,0.004203748,0.0009843335],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.1575566,0.77152,0.02637538,0.002293069,0.001303026,0.006990131,0.0002672358,0.0001806334,0.03351391],"genre_scores_gemma":[0.2739202,0.7122704,0.008748904,0.004535123,0.00001682037,0.000125767,0.0003373005,0.00003000934,0.00001539928],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.2967669,"threshold_uncertainty_score":0.6806528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04399281830942545,"score_gpt":0.3475951513406365,"score_spread":0.303602333031211,"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."}}