{"id":"W4405488613","doi":"10.1109/embc53108.2024.10781723","title":"Predicting Donor Selection and Multi-Organ Transplantation within Organ Procurement Organizations Using Machine Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Blood donation and transfusion practices","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; Toronto Rehabilitation Institute; University of Toronto","funders":"","keywords":"Organ procurement; Selection (genetic algorithm); Computer science; Transplantation; Procurement; Machine learning; Organ transplantation; Artificial intelligence; Organ donation; Medicine; Business; Internal medicine; Marketing","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":[0.004636264,0.0005652641,0.0003847466,0.00214308,0.0003651609,0.001604102,0.0005718796,0.0005802134,0.0006750215],"category_scores_gemma":[0.00945376,0.0001533368,0.0005718496,0.001694353,0.0002286492,0.0009990052,0.001004156,0.0009117298,0.0004051864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001317658,"about_ca_system_score_gemma":0.001661941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01324328,"about_ca_topic_score_gemma":0.01934143,"domain_scores_codex":[0.9986138,0.0005941206,0.0001346285,0.000208252,0.0002019996,0.0002470666],"domain_scores_gemma":[0.9923769,0.004859237,0.001150546,0.0004169067,0.0007655647,0.0004308351],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001558946,0.0003983605,0.8327488,0.00006582792,0.0001147867,0.0001433393,0.0001406772,0.10744,0.0002829085,0.0007903213,0.003332723,0.05438635],"study_design_scores_gemma":[0.00002279779,0.0001751654,0.1787066,0.00008275209,0.00006330112,0.0001618081,0.0006683473,0.8125902,0.001604124,0.002620779,0.003268933,0.00003517812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9865953,0.0006988475,0.008521097,0.00075003,0.00003869307,0.00004959748,0.001803926,0.0001103647,0.001432291],"genre_scores_gemma":[0.990263,0.0002251742,0.005464388,0.00005570074,0.00002901342,0.00002306503,0.003529305,0.000006363988,0.0004040079],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01324328,"threshold_uncertainty_score":0.02633238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01880868238213666,"score_gpt":0.2457043128999796,"score_spread":0.226895630517843,"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."}}