{"id":"W2026883984","doi":"10.1213/01.ane.0000132928.45858.92","title":"Predicting Allogeneic Blood Transfusion Use in Total Joint Arthroplasty","year":2004,"lang":"en","type":"article","venue":"Anesthesia & Analgesia","topic":"Blood transfusion and management","field":"Medicine","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Medicine; Joint arthroplasty; Logistic regression; Receiver operating characteristic; Blood transfusion; Arthroplasty; Retrospective cohort study; Joint replacement; Predictive modelling; Intensive care medicine; Surgery; Emergency medicine; Internal medicine; Machine learning; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002514366,0.0002689158,0.0004198329,0.0002936122,0.0001013065,0.00003737261,0.00006905273,0.0001488683,0.000152695],"category_scores_gemma":[0.00001980795,0.0002396752,0.0001983474,0.0004339247,0.00006747456,0.0001706808,0.0000163178,0.0002909692,0.00005146662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000557538,"about_ca_system_score_gemma":0.00009972892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00116789,"about_ca_topic_score_gemma":0.001014115,"domain_scores_codex":[0.9981568,0.00005297853,0.0004636942,0.0004812301,0.0004042911,0.0004409548],"domain_scores_gemma":[0.9992778,0.00002211526,0.00006088075,0.0003712687,0.00004961662,0.0002182639],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008858608,0.005520836,0.8915594,0.0004798678,0.0005534245,0.02080224,0.003790757,0.001827951,0.05459423,0.006642936,0.0002830949,0.01305946],"study_design_scores_gemma":[0.007334781,0.0007638854,0.9789351,0.0004082043,0.0004343655,0.002799357,0.0003855692,0.0001026724,0.006888988,0.0001047418,0.001512378,0.000329933],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9937444,0.0002495603,0.0003371008,0.004329433,0.00004497352,0.0007172006,8.47635e-7,0.000163822,0.000412703],"genre_scores_gemma":[0.9961209,0.0006172886,0.00120954,0.001536514,0.00008347139,0.00003795258,0.0000210378,0.00004335139,0.0003299639],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08737578,"threshold_uncertainty_score":0.9773669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01745226441801047,"score_gpt":0.2250289553706904,"score_spread":0.2075766909526799,"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."}}