{"id":"W4376959749","doi":"10.1016/j.jhep.2023.01.006","title":"Artificial intelligence, machine learning, and deep learning in liver transplantation","year":2023,"lang":"en","type":"review","venue":"Journal of Hepatology","topic":"Organ Transplantation Techniques and Outcomes","field":"Medicine","cited_by":214,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto General Hospital; Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"Canadian Institutes of Health Research","keywords":"Artificial intelligence; Context (archaeology); Medicine; Machine learning; Candidacy; Transplantation; Liver transplantation; Intensive care medicine; Computer science; Surgery","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004901883,0.0002041245,0.001425278,0.0008374658,0.0000386803,0.00001176358,0.00007195531,0.0003226562,0.0001161388],"category_scores_gemma":[0.0001077653,0.0001548806,0.0002442953,0.0002821536,0.00005580133,0.00004910436,0.000009898303,0.001285802,0.00002591643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005752925,"about_ca_system_score_gemma":0.0001369332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002651967,"about_ca_topic_score_gemma":0.0001226278,"domain_scores_codex":[0.9981663,0.0002622115,0.001078618,0.0001547494,0.0001600724,0.0001780336],"domain_scores_gemma":[0.9988633,0.0003650711,0.000583764,0.00004807781,0.00005940583,0.00008038514],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001060111,0.00003873492,0.00854288,0.0209186,0.0001079553,0.004125373,0.0003263884,0.000002102041,9.441436e-7,0.0001583717,0.000006840721,0.9656658],"study_design_scores_gemma":[0.000704974,0.002423495,0.0009638189,0.02319315,0.006352545,0.05906681,0.0001888838,0.0007630565,0.0001390015,0.000732944,0.9047517,0.0007195976],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0004788112,0.9971967,0.00169865,0.0001054053,0.000157176,0.0002192534,0.000003485745,0.00004414624,0.00009641417],"genre_scores_gemma":[0.0006686104,0.998168,0.0007911177,0.0000296238,0.0001010315,0.000004053008,0.00004376958,0.00003957065,0.0001542586],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9649462,"threshold_uncertainty_score":0.6315846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06250269291731594,"score_gpt":0.3725299567619902,"score_spread":0.3100272638446743,"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."}}