{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002790511,0.0003291339,0.0004305007,0.0006371542,0.000272415,0.001396691,0.0006195342,0.0007650433,0.004403647],"category_scores_gemma":[0.02408079,0.0001622729,0.0004642097,0.0005488074,0.0006377754,0.001618728,0.0009171658,0.001823169,0.0004984838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001095855,"about_ca_system_score_gemma":0.001549989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002270358,"about_ca_topic_score_gemma":0.003377051,"domain_scores_codex":[0.9987265,0.000767631,0.00005293876,0.0001141767,0.0002209104,0.0001178011],"domain_scores_gemma":[0.9853393,0.01160504,0.001244349,0.0004953138,0.0009431003,0.0003728828],"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":[0.0007297965,0.0008854606,0.0360997,0.00072008,0.0004386166,0.0001016963,0.0001579083,0.04968154,0.0004174335,0.01258151,0.03001956,0.8681667],"study_design_scores_gemma":[0.0006611385,0.002386677,0.074015,0.002501484,0.0009010303,0.0005856417,0.0009816855,0.5151372,0.005236203,0.3508847,0.04649678,0.0002125142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5267671,0.05794163,0.167536,0.1794797,0.004021767,0.000304507,0.001569913,0.001513579,0.06086576],"genre_scores_gemma":[0.9729255,0.005932557,0.01409281,0.00289655,0.001200566,0.00004969292,0.0002903428,0.00003086537,0.002581104],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004403647,"threshold_uncertainty_score":0.01475781,"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."}}