{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000219516,0.0001439401,0.0003386223,0.0001847241,0.0003192876,0.00001889821,0.00007520047,0.00004818878,0.0003513834],"category_scores_gemma":[0.00001042432,0.0001314541,0.00005015042,0.0001160706,0.00006810987,0.00004776568,0.00005946541,0.0001826185,0.00001886771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004256324,"about_ca_system_score_gemma":0.00001752636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001958774,"about_ca_topic_score_gemma":0.00001169941,"domain_scores_codex":[0.9988233,0.00008185262,0.0002806196,0.00044863,0.0001353738,0.0002301649],"domain_scores_gemma":[0.9994831,0.00005218841,0.00005645207,0.0003174325,0.00001927143,0.00007157514],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007044122,0.0002109449,0.9380518,0.0001672857,0.0009193514,0.0003134414,0.005405911,0.000002111009,0.04128222,0.0004609934,0.0002626973,0.0122189],"study_design_scores_gemma":[0.002994196,0.0009480609,0.9854833,0.0000682338,0.00022594,0.0004085787,0.0002002288,5.665793e-7,0.002838691,0.0002467566,0.006443392,0.0001420942],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970101,0.0008725683,0.00002195153,0.001099697,0.0001293957,0.0004222414,0.00008512728,0.00004400509,0.0003149332],"genre_scores_gemma":[0.9970534,0.0004922582,0.0003137808,0.0003576276,0.00003397337,0.00008993559,0.0002277368,0.00001951827,0.001411801],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04743153,"threshold_uncertainty_score":0.5360544,"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."}}