{"id":"W4315630296","doi":"10.1016/j.ajt.2022.12.002","title":"Machine learning–based mortality prediction models using national liver transplantation registries are feasible but have limited utility across countries","year":2023,"lang":"en","type":"article","venue":"American Journal of Transplantation","topic":"Organ Transplantation Techniques and Outcomes","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hospital for Sick Children; University Health Network","funders":"NHS Blood and Transplant","keywords":"Medicine; Liver transplantation; Receiver operating characteristic; Transplantation; Machine learning; Demography; Internal medicine; Computer science","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":[0.01451759,0.001294258,0.001850141,0.003063398,0.0006021148,0.002674718,0.001343553,0.0008477176,0.0029842],"category_scores_gemma":[0.04975586,0.0005037889,0.001835158,0.005881188,0.000462552,0.002861466,0.001463875,0.001900254,0.001648399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006928698,"about_ca_system_score_gemma":0.001691018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009034305,"about_ca_topic_score_gemma":0.011877,"domain_scores_codex":[0.9889635,0.007135157,0.001007332,0.001531714,0.001022037,0.0003402683],"domain_scores_gemma":[0.9487665,0.02767988,0.01018714,0.006787333,0.005944701,0.0006343681],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004063599,0.0004818846,0.8196865,0.0005938771,0.004081891,0.00006674643,0.00009717775,0.02458445,0.0002516607,0.00174749,0.01400709,0.1339949],"study_design_scores_gemma":[0.0003698022,0.001111383,0.7156324,0.00175838,0.003005278,0.0005175122,0.0007022645,0.2377407,0.002014849,0.01738502,0.01957764,0.0001847009],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7243823,0.008551342,0.1683665,0.009886766,0.001152982,0.0007152008,0.06119826,0.001563527,0.02418307],"genre_scores_gemma":[0.9268349,0.001953939,0.04011051,0.001346748,0.0003674305,0.0003538411,0.02800454,0.00008572155,0.0009423272],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01451759,"threshold_uncertainty_score":0.07677728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06501070194239665,"score_gpt":0.3371630097582711,"score_spread":0.2721523078158745,"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."}}