{"id":"W4313454553","doi":"10.1111/ctr.14903","title":"Comparable kidney transplant outcomes in selected patients with a body mass index ≥ 40: A personalized medicine approach to recipient selection","year":2023,"lang":"en","type":"article","venue":"Clinical Transplantation","topic":"Renal Transplantation Outcomes and Treatments","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Medicine; Selection (genetic algorithm); Body mass index; Kidney transplantation; Kidney transplant; Index (typography); Personalized medicine; Kidney; Internal medicine; Family medicine; Intensive care medicine; Bioinformatics; Artificial intelligence; World Wide Web","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005124561,0.0003682992,0.0009527463,0.0005597448,0.0001172744,0.000020448,0.000113836,0.0002059683,0.00005950126],"category_scores_gemma":[0.00009120512,0.0002559035,0.0001553476,0.001910271,0.0001105151,0.0001445748,0.000003309814,0.0003969115,0.00006170347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001143074,"about_ca_system_score_gemma":0.0001512726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001233439,"about_ca_topic_score_gemma":0.00005342208,"domain_scores_codex":[0.9967377,0.0002328267,0.001050042,0.0007157293,0.0007624506,0.0005012939],"domain_scores_gemma":[0.9984723,0.0004109844,0.0001496508,0.0002036429,0.000229802,0.0005335895],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.009002406,0.001084073,0.9870211,0.0004658848,0.0003485181,0.00008104562,0.0009615531,0.0003616486,0.0002179399,0.00009559208,0.000109879,0.0002502851],"study_design_scores_gemma":[0.0265895,0.00144816,0.9674621,0.000932743,0.0004613983,0.00002954514,0.00006809985,0.002453035,0.00008682417,0.00003346394,0.000161758,0.0002733821],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9802709,0.000008346025,0.01439392,0.0008770478,0.0002401472,0.002027136,0.0001913263,0.0002832374,0.001707915],"genre_scores_gemma":[0.9911687,0.0002964285,0.002532613,0.0008996951,0.00006383573,0.0002166981,0.003973,0.00005159212,0.0007974892],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01955909,"threshold_uncertainty_score":0.9999893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04944331407033101,"score_gpt":0.3542588558545317,"score_spread":0.3048155417842007,"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."}}