{"id":"W4210532306","doi":"10.1016/j.humimm.2022.01.007","title":"hlaR: A rapid and reproducible tool to identify eplet mismatches between transplant donors and recipients","year":2022,"lang":"en","type":"article","venue":"Human Immunology","topic":"Renal Transplantation Outcomes and Treatments","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"National Institute on Minority Health and Health Disparities; National Institute of Allergy and Infectious Diseases","keywords":"Computer science; Imputation (statistics); Data mining; Artificial intelligence; Machine learning; Missing data","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":[0.007195154,0.001108825,0.001051114,0.005885748,0.0004317918,0.002234351,0.001062587,0.001431476,0.008207081],"category_scores_gemma":[0.0134932,0.0005548433,0.0005787083,0.001596803,0.0004116371,0.001318106,0.00172566,0.001404761,0.004804412],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003600087,"about_ca_system_score_gemma":0.0005532649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004930064,"about_ca_topic_score_gemma":0.0007456093,"domain_scores_codex":[0.9936493,0.00216143,0.0006664603,0.001351363,0.001770641,0.0004009148],"domain_scores_gemma":[0.9891428,0.004620372,0.002542864,0.001318922,0.001840344,0.000534698],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00378734,0.0008106549,0.4539602,0.0009740192,0.0006232192,0.001307466,0.0009102715,0.002104964,0.1473342,0.002406495,0.03613082,0.3496502],"study_design_scores_gemma":[0.0007975725,0.003064199,0.5367976,0.0006875453,0.0008535529,0.01551944,0.001075143,0.05437523,0.2699264,0.00502266,0.1113315,0.0005490127],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6448323,0.005522463,0.2804119,0.001682386,0.001459493,0.002230778,0.02180012,0.0175181,0.02454249],"genre_scores_gemma":[0.7337984,0.0008836651,0.2399482,0.00155407,0.000635317,0.001906917,0.009612864,0.00121043,0.01045008],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.008207081,"threshold_uncertainty_score":0.03805208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04175773480101252,"score_gpt":0.3264286936736966,"score_spread":0.2846709588726841,"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."}}