{"id":"W2921301581","doi":"10.1111/trf.15160","title":"Addressing the identity crisis in healthcare: positive patient identification technology reduces wrong patient events","year":2019,"lang":"en","type":"letter","venue":"Transfusion","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Identity crisis; Identity (music); Identification (biology); Health care; Medicine; Patient care; Medical emergency; Psychology; Nursing; Political science; Social psychology; Law","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":["metaepi_narrow","sts","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.001862875,0.0005723512,0.000969555,0.0008849689,0.00138704,0.00003304422,0.0007713302,0.002724735,0.0001187585],"category_scores_gemma":[0.0001651919,0.000452464,0.0002012441,0.001202916,0.00008451654,0.000383148,0.0001702925,0.008765198,0.0005640597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002846558,"about_ca_system_score_gemma":0.001494499,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01864048,"about_ca_topic_score_gemma":0.005654659,"domain_scores_codex":[0.988808,0.004440257,0.00255212,0.001214654,0.001242812,0.00174212],"domain_scores_gemma":[0.9959674,0.0008002502,0.001417218,0.001240412,0.0004804686,0.00009418182],"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":[0.0002706687,0.0002789735,0.01140086,0.006007132,0.0001326948,0.0001199027,0.02459014,0.00001562795,0.001915946,0.0002779502,0.9016935,0.05329664],"study_design_scores_gemma":[0.005664505,0.003181008,0.03269333,0.0344393,0.0004858596,0.0001191787,0.02608977,0.0003614845,0.003555436,0.0101012,0.8794016,0.003907301],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2673479,0.002049465,0.0002119716,0.7171725,0.005430371,0.007248939,0.0001474881,0.0001688498,0.0002224821],"genre_scores_gemma":[0.6797525,0.001954118,0.00005065599,0.3128417,0.001537625,0.002309147,0.000656893,0.0002118156,0.0006856116],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.4124045,"threshold_uncertainty_score":0.999913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0833599020508544,"score_gpt":0.4185088564383242,"score_spread":0.3351489543874698,"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."}}