{"id":"W2905467199","doi":"10.1111/trf.15102","title":"Electronic patient identification for sample labeling reduces wrong blood in tube errors","year":2018,"lang":"en","type":"article","venue":"Transfusion","topic":"Blood transfusion and management","field":"Medicine","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Paul's Hospital; Canadian Blood Services; McMaster University","funders":"","keywords":"Medicine; Barcode; Identification (biology); Sample (material); Electronic data; Emergency medicine; Database; Computer science","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":[0.0003046365,0.000135182,0.0001941615,0.0001893097,0.0001271793,0.00001519735,0.00007355788,0.00007985228,0.0001851355],"category_scores_gemma":[0.00004529548,0.0001245669,0.00007830471,0.000342985,0.00004244448,0.00008071838,0.000009792793,0.0001325811,0.00001785943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005080958,"about_ca_system_score_gemma":0.00006133371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003146143,"about_ca_topic_score_gemma":0.0009958412,"domain_scores_codex":[0.9986768,0.00002925279,0.0003662703,0.0003567356,0.0002213758,0.0003495941],"domain_scores_gemma":[0.9995042,0.00004309194,0.00004044859,0.0002220595,0.0001181828,0.0000720165],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001156761,0.001287224,0.001820946,0.0002392436,0.0001064498,0.000005458512,0.004540622,0.00001864433,0.8825766,0.001854756,0.0004172611,0.1059761],"study_design_scores_gemma":[0.01051586,0.0044952,0.01318345,0.0004621332,0.000738477,0.00002053743,0.001654509,0.0018037,0.9340383,0.00162259,0.03098224,0.0004830255],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9903921,0.0002332895,0.005254382,0.001827468,0.0003223755,0.001483362,0.000006677864,0.0000904467,0.0003899389],"genre_scores_gemma":[0.9969507,0.0007443342,0.001517409,0.000322313,0.0001035544,0.00009381488,0.00006885199,0.00002699099,0.0001720732],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.105493,"threshold_uncertainty_score":0.5079692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01588743213799667,"score_gpt":0.2757905700180024,"score_spread":0.2599031378800058,"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."}}