{"id":"W4387007964","doi":"10.1016/j.humimm.2023.08.049","title":"P222 Using epitope analysis to aid in test interpretation and to guide donor matching for a highly sensitized patient","year":2023,"lang":"en","type":"article","venue":"Human Immunology","topic":"Transplantation: Methods and Outcomes","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Matching (statistics); Epitope; Interpretation (philosophy); Test (biology); Computer science; Computational biology; Medicine; Artificial intelligence; Immunology; Biology; Pathology; Programming language; Antigen","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":[0.001602364,0.0006017591,0.0002852138,0.001057903,0.0003723055,0.001661771,0.0005698943,0.0007140988,0.003382738],"category_scores_gemma":[0.002645909,0.0002032606,0.0003364365,0.0004129073,0.0002912767,0.0006545079,0.0006284428,0.0008565036,0.001880774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000287764,"about_ca_system_score_gemma":0.0004442995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006908259,"about_ca_topic_score_gemma":0.0009033904,"domain_scores_codex":[0.9990804,0.000342709,0.00007068174,0.0001174925,0.000247366,0.0001413894],"domain_scores_gemma":[0.9991453,0.000308159,0.0001269715,0.0001035567,0.0002195665,0.00009654427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001585238,0.0007692102,0.6299525,0.0002389305,0.0001316282,0.007264939,0.0004873778,0.0009790846,0.09609882,0.002371106,0.01016163,0.2499594],"study_design_scores_gemma":[0.0002568554,0.003073232,0.4757692,0.0008491258,0.0006179133,0.07555977,0.001668511,0.04133128,0.3041093,0.01108798,0.0855327,0.0001440343],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8743691,0.003832455,0.07158886,0.002506552,0.0006387043,0.0006176863,0.0007395613,0.0009439997,0.04476302],"genre_scores_gemma":[0.9710251,0.0008051358,0.02360088,0.0009013722,0.0001853344,0.0001009993,0.0003164914,0.0001072989,0.002957386],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003382738,"threshold_uncertainty_score":0.01131642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03219678637444972,"score_gpt":0.3701002696322991,"score_spread":0.3379034832578494,"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."}}