{"id":"W2909783538","doi":"10.1111/trf.15066","title":"Translating red cell “omics” into new perspectives in transfusion medicine: mining the gems in the data mountains","year":2019,"lang":"en","type":"letter","venue":"Transfusion","topic":"Erythrocyte Function and Pathophysiology","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Blood Services; University of British Columbia","funders":"","keywords":"Transfusion medicine; Omics; Medicine; Computational biology; Data science; Computer science; Bioinformatics; Blood transfusion; Biology; Immunology","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":[0.01114775,0.0007854459,0.001619772,0.0009174602,0.004275152,0.006282524,0.001653606,0.03300024,0.005763364],"category_scores_gemma":[0.05977124,0.0008095464,0.001048348,0.0008016144,0.004737857,0.006036815,0.00267723,0.04514915,0.005051143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004116019,"about_ca_system_score_gemma":0.004964593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005069997,"about_ca_topic_score_gemma":0.01087375,"domain_scores_codex":[0.993122,0.003429028,0.0008175187,0.0006685642,0.001526559,0.0004363506],"domain_scores_gemma":[0.9460708,0.04177673,0.001789109,0.00141583,0.005219962,0.003727534],"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":[0.00003835677,0.00002145888,0.0004639193,0.00004476152,0.00002060337,0.0006650073,0.0001794887,0.00004039638,0.0001406687,0.003744259,0.9848576,0.009783569],"study_design_scores_gemma":[0.00009861413,0.00004979172,0.001298005,0.0005097646,0.00004435711,0.001435761,0.000756888,0.0008363185,0.0003576157,0.05523989,0.9392874,0.00008550823],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"other","genre_scores_codex":[0.00009774361,0.0007367912,0.000157056,0.9889053,0.009574785,0.000003529197,0.00003310974,0.00001503487,0.0004765395],"genre_scores_gemma":[0.00213769,0.001236959,0.0006873586,0.9412357,0.05181324,0.00002083709,0.0000269606,0.00003416565,0.00280711],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.03300024,"threshold_uncertainty_score":0.05895567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06026481057936789,"score_gpt":0.3035810853683213,"score_spread":0.2433162747889534,"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."}}