{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008310196,0.0004145516,0.0002975227,0.001566419,0.0003277418,0.001202874,0.0009419193,0.000577207,0.002479659],"category_scores_gemma":[0.0520402,0.0002655268,0.0003512875,0.001485121,0.0004313354,0.0009821099,0.001023671,0.0004853397,0.0006632984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000734096,"about_ca_system_score_gemma":0.001547465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002032431,"about_ca_topic_score_gemma":0.002878634,"domain_scores_codex":[0.9896499,0.005203416,0.001095111,0.0007517705,0.002860451,0.0004392709],"domain_scores_gemma":[0.9473789,0.0230072,0.01813859,0.003665626,0.007130521,0.0006790676],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004837899,0.0004085666,0.8887858,0.0001652373,0.00004317466,0.00006133782,0.0003137992,0.001023742,0.001608419,0.0001067848,0.002068307,0.104931],"study_design_scores_gemma":[0.00006811161,0.001976582,0.9693863,0.0002278693,0.000141739,0.0008744158,0.0006662744,0.007259055,0.0150467,0.0002255389,0.004098454,0.00002882625],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9851627,0.0006857224,0.009671411,0.0007851273,0.00006228185,0.0001813326,0.0005222669,0.0003485573,0.00258062],"genre_scores_gemma":[0.9868268,0.0003209545,0.01170027,0.0002217106,0.00004413012,0.00006421073,0.0004289927,0.00003468094,0.0003581894],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008310196,"threshold_uncertainty_score":0.04394907,"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."}}