{"id":"W4210295099","doi":"10.1016/j.humimm.2022.01.002","title":"Genome Canada precision medicine strategy for structured national implementation of epitope matching in renal transplantation","year":2022,"lang":"en","type":"article","venue":"Human Immunology","topic":"Renal Transplantation Outcomes and Treatments","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Dalhousie University; McGill University; University of British Columbia","funders":"","keywords":"Epitope; Immunogenicity; Human leukocyte antigen; Transplantation; Antigenicity; Computational biology; Medicine; Matching (statistics); Immunology; Computer science; Antibody; Antigen; Biology; Internal medicine; Pathology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0394446,0.0007843755,0.0007835956,0.00237419,0.002585119,0.004661283,0.003954267,0.005988122,0.01777156],"category_scores_gemma":[0.04815038,0.000492931,0.001388343,0.00236515,0.001904794,0.001641582,0.00572617,0.005811374,0.0027784],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0325124,"about_ca_system_score_gemma":0.2289565,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5256619,"about_ca_topic_score_gemma":0.5661286,"domain_scores_codex":[0.9806514,0.007912897,0.0007335583,0.001115822,0.007362643,0.002223596],"domain_scores_gemma":[0.9527587,0.008535953,0.00226563,0.003408634,0.02098741,0.01204358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003161774,0.0003140761,0.00939433,0.0008120807,0.0001881717,0.0003113687,0.0006184363,0.002583611,0.001696944,0.06772319,0.638636,0.2774057],"study_design_scores_gemma":[0.0004361954,0.0003373413,0.02548353,0.001641287,0.0001373153,0.0001911722,0.000462711,0.001857097,0.001353779,0.02021085,0.9477945,0.0000941921],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"methods","genre_scores_codex":[0.007602933,0.01360333,0.05023025,0.7575735,0.01247881,0.003587164,0.009522873,0.002373457,0.1430277],"genre_scores_gemma":[0.138234,0.01746534,0.2799667,0.4687121,0.005542098,0.005057597,0.01168752,0.0005300951,0.07280469],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9674876,"threshold_uncertainty_score":0.954263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03392475728467877,"score_gpt":0.3445507075227962,"score_spread":0.3106259502381175,"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."}}