{"id":"W4416217431","doi":"10.1016/j.landig.2025.100932","title":"Reducing futile donation after circulatory death procurement with machine learning","year":2025,"lang":"en","type":"article","venue":"The Lancet Digital Health","topic":"Blood donation and transfusion practices","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Rehabilitation Institute; University Health Network","funders":"Novo Nordisk; Eisai; Natera; CareDx","keywords":"Procurement; Donation; Organ procurement; Organ donation","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":[],"consensus_categories":[],"category_scores_codex":[0.0004758992,0.0001109984,0.0001581069,0.0001092796,0.0003067732,0.0004415,0.0001323543,0.00002204621,0.0000725818],"category_scores_gemma":[0.00004841757,0.00007202331,0.00002738924,0.0003383235,0.00002404119,0.001152853,0.00004952646,0.0002165722,0.00005356866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003336104,"about_ca_system_score_gemma":0.00007179512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002709259,"about_ca_topic_score_gemma":0.000148053,"domain_scores_codex":[0.9991326,0.00001603378,0.0001865676,0.0001954259,0.0002387883,0.0002306317],"domain_scores_gemma":[0.9995367,0.00004928671,0.0001557384,0.0001635073,0.00008382234,0.0000109701],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001077769,0.0005382453,0.5403583,0.001945409,0.0002050523,0.00001221661,0.0007243293,0.0009814366,0.00002552742,0.1558882,0.005948377,0.2922951],"study_design_scores_gemma":[0.002096906,0.00004706629,0.2377864,0.0004470795,0.00006061642,0.000006130997,0.0004150445,0.005878132,0.00002004887,0.005219033,0.7477264,0.0002971186],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5700594,0.002299215,0.002892227,0.09776231,0.0006053039,0.001415504,0.00001619292,0.000745466,0.3242044],"genre_scores_gemma":[0.9894747,0.00003633689,0.0000552557,0.009285436,0.0003264788,0.00003802533,0.00003814345,0.00001235917,0.000733307],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7417781,"threshold_uncertainty_score":0.4257395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02552187972755695,"score_gpt":0.2706570129368564,"score_spread":0.2451351332092994,"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."}}