{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001864638,0.0001668587,0.0003767253,0.0007819355,0.0002648419,0.0008232166,0.0001964541,0.0002477345,0.00119601],"category_scores_gemma":[0.00263013,0.00008751259,0.0004538266,0.0008178935,0.0001993644,0.0004646899,0.0004230804,0.0003402558,0.0001383951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002652235,"about_ca_system_score_gemma":0.0002533222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000461651,"about_ca_topic_score_gemma":0.0009232145,"domain_scores_codex":[0.9992618,0.0003765372,0.00007960611,0.00009995628,0.0001070953,0.00007505309],"domain_scores_gemma":[0.9986338,0.0003800425,0.0006911029,0.00008241914,0.00008079396,0.0001318221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002729498,0.00004086431,0.9885211,0.0000276486,0.0001335096,0.00008503577,0.00006654729,0.0001598053,0.0005787276,0.00005442034,0.0001725101,0.009886869],"study_design_scores_gemma":[0.00002659249,0.000319758,0.9969082,0.00003320412,0.0001497153,0.0004195663,0.000250912,0.0008375623,0.0003652002,0.0002728559,0.0004088771,0.000007484286],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973105,0.0009137127,0.0006506459,0.0003457702,0.00001692955,0.00001731367,0.0001454101,0.000007860876,0.0005917939],"genre_scores_gemma":[0.9992899,0.0001719663,0.0002795898,0.00009632978,0.00003108708,0.000007881196,0.0000821849,0.00000138917,0.00003971134],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001864638,"threshold_uncertainty_score":0.00986129,"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."}}