{"id":"W4387798706","doi":"10.1111/trf.17585","title":"An unsupervised learning approach to identify immunoglobulin utilization patterns using electronic health records","year":2023,"lang":"en","type":"article","venue":"Transfusion","topic":"Blood donation and transfusion practices","field":"Business, Management and Accounting","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kingston Health Sciences Centre; Canadian Blood Services; Health Sciences Centre; Ontario Stroke Network; Sunnybrook Health Science Centre; University of Toronto; Queen's University; McMaster University; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Canadian Blood Services; Calgary Foundation","keywords":"Medicine; Economic shortage; Cluster (spacecraft); Demographics; Health records; Cluster analysis; Electronic health record; Emergency medicine; Health care; Demography; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001186395,0.000221447,0.0002368086,0.000632221,0.0006997992,0.0003836088,0.0002676496,0.0000971269,0.0003973163],"category_scores_gemma":[0.00003398878,0.0002283219,0.00008758908,0.001899212,0.00001333475,0.002143532,0.00003882884,0.0003248611,0.0003134483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006211922,"about_ca_system_score_gemma":0.00006896063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00376576,"about_ca_topic_score_gemma":0.0005679181,"domain_scores_codex":[0.9979824,0.00009777818,0.0004161341,0.0005185308,0.0004521475,0.0005329444],"domain_scores_gemma":[0.9993994,0.00002832211,0.0001368534,0.0002530926,0.0001371751,0.00004514238],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001285993,0.002967206,0.1256487,0.002465171,0.0002521689,0.00002426575,0.007900551,0.1123724,0.1065045,0.03220912,0.001161349,0.6072087],"study_design_scores_gemma":[0.006104304,0.0003864224,0.2977536,0.0004417235,0.000299615,0.00002121149,0.008740885,0.5664422,0.001186542,0.001697366,0.1150017,0.001924442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9160513,0.0000717411,0.07844392,0.001299081,0.0003617499,0.0006177263,0.00000334333,0.0007613476,0.002389756],"genre_scores_gemma":[0.9965489,0.0006940084,0.0003004273,0.001522707,0.0003308586,0.00002747786,0.0004093868,0.00006321158,0.0001030556],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6052842,"threshold_uncertainty_score":0.9310695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06471953312103576,"score_gpt":0.3312545889771531,"score_spread":0.2665350558561174,"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."}}