{"id":"W3129378865","doi":"10.1111/vox.13089","title":"Application of unsupervised machine learning to identify areas of blood product wastage in transfusion medicine","year":2021,"lang":"en","type":"article","venue":"Vox Sanguinis","topic":"Blood donation and transfusion practices","field":"Business, Management and Accounting","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nova Scotia Health Authority; Dalhousie University","funders":"","keywords":"Blood bank; Blood product; Association rule learning; Transfusion medicine; Medicine; Blood transfusion; Computer science; Nova scotia; Product (mathematics); Artificial intelligence; Operations management; Machine learning; Emergency medicine; Surgery; Engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002165535,0.0003135498,0.0003000837,0.003316469,0.000352565,0.0007389963,0.0004654606,0.0003607453,0.0005941548],"category_scores_gemma":[0.009770682,0.0001416612,0.0003461406,0.002327825,0.0003439914,0.0003699844,0.0005345352,0.0004227274,0.0001725248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007130787,"about_ca_system_score_gemma":0.00154661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008840559,"about_ca_topic_score_gemma":0.01197095,"domain_scores_codex":[0.9986296,0.0006700029,0.0001454933,0.0002100811,0.0002682506,0.00007673731],"domain_scores_gemma":[0.9907295,0.005981699,0.00149544,0.000426412,0.001197104,0.000169794],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002312863,0.0003748009,0.736308,0.000227628,0.000264021,0.000406327,0.000733458,0.02579993,0.004271901,0.0007375089,0.00128418,0.229361],"study_design_scores_gemma":[0.00003638535,0.0004164539,0.5189998,0.0001302291,0.0001704795,0.0009896512,0.0009471114,0.4631597,0.005930974,0.005680989,0.003485548,0.00005266938],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8762018,0.0005894059,0.1179248,0.0004848552,0.00004601688,0.0003159906,0.001035061,0.0004394068,0.002962559],"genre_scores_gemma":[0.9401507,0.0001396516,0.0587047,0.0000514554,0.00002003612,0.00008020976,0.0004537004,0.00001151808,0.0003879839],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008840559,"threshold_uncertainty_score":0.01757818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0175594545557198,"score_gpt":0.2745071866287025,"score_spread":0.2569477320729827,"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."}}