{"id":"W4389887102","doi":"10.1097/txd.0000000000001565","title":"Canadian Kidney Transplant Professionals’ Perspectives on Precision Medicine and Molecular Matching in Kidney Allocation","year":2023,"lang":"en","type":"article","venue":"Transplantation Direct","topic":"Renal Transplantation Outcomes and Treatments","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Population and Public Health; University of British Columbia; Translational Research in Oncology; Université de Montréal; McGill University; Centre Hospitalier de l’Université de Montréal","funders":"Genome Alberta; Canadian Institutes of Health Research; Genome British Columbia; Genome Canada","keywords":"Medicine; Kidney transplant; Matching (statistics); Precision medicine; Kidney; Kidney transplantation; Intensive care medicine; Family medicine; Internal medicine; Pathology","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":[],"consensus_categories":[],"category_scores_codex":[0.0003116738,0.0002315744,0.0003245921,0.0008414519,0.0001172132,0.00001579634,0.00006029869,0.0001112495,0.0001157415],"category_scores_gemma":[0.00005305006,0.0001820809,0.00006090838,0.0005655063,0.00004228868,0.0001212029,0.000002056445,0.0001818353,0.00005930469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001508572,"about_ca_system_score_gemma":0.000255556,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01025411,"about_ca_topic_score_gemma":0.00333042,"domain_scores_codex":[0.9984053,0.0001121845,0.0003436493,0.0004269546,0.0004033437,0.0003085539],"domain_scores_gemma":[0.9989877,0.0001898383,0.00005196323,0.00015404,0.00005380508,0.0005626522],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.01235405,0.002008797,0.3599637,0.01306551,0.001823396,0.01648026,0.410034,0.002237652,0.1318406,0.02858511,0.005119032,0.01648788],"study_design_scores_gemma":[0.02264426,0.001224105,0.91707,0.02917912,0.0009354914,0.0005222543,0.006556401,0.001449473,0.01644557,0.001965528,0.0009636671,0.001044165],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9612535,0.0002650481,0.0003555802,0.02101073,0.0002533688,0.001249472,0.0003634265,0.0002229846,0.01502584],"genre_scores_gemma":[0.9921446,0.003324963,0.0002276313,0.002075325,0.00003196152,0.00007405842,0.001578603,0.00003308542,0.0005097463],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5571063,"threshold_uncertainty_score":0.9963367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02032349645940213,"score_gpt":0.3157656490977108,"score_spread":0.2954421526383086,"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."}}