{"id":"W4386097786","doi":"10.3389/ti.2023.11589","title":"Delphi: A Democratic and Cost-Effective Method of Consensus Generation in Transplantation","year":2023,"lang":"en","type":"article","venue":"Transplant International","topic":"Renal Transplantation Outcomes and Treatments","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Hôpital Maisonneuve-Rosemont; Ontario Brain Institute; University Health Network","funders":"Alexion Pharmaceuticals","keywords":"Medicine; Delphi method; Medical diagnosis; Delphi; Medical physics; Transplantation; Pathology; Surgery; Computer science; Artificial intelligence","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":[0.0001936921,0.0001152349,0.000213682,0.0003207979,0.00002418934,0.000007568739,0.00003745513,0.00006003509,0.00004276455],"category_scores_gemma":[0.00001107342,0.0001000819,0.00006006142,0.0001985217,0.00003104441,0.00005302414,0.000002270755,0.00008314986,0.00001051956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003438456,"about_ca_system_score_gemma":0.00002817663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009747548,"about_ca_topic_score_gemma":0.0001265133,"domain_scores_codex":[0.9990654,0.00006010093,0.0003187993,0.0001946261,0.0002390267,0.0001220538],"domain_scores_gemma":[0.9995212,0.0002448369,0.00005891937,0.00006038156,0.00006093723,0.00005374065],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005426296,0.0008007369,0.6596332,0.002146399,0.001557577,0.003064454,0.01238087,0.007315209,0.213366,0.01036912,0.0001788592,0.08376122],"study_design_scores_gemma":[0.01104503,0.0003032417,0.8615148,0.0007576096,0.0003897595,0.001483267,0.0001263211,0.08395343,0.03950704,0.0005842086,0.0001128214,0.000222436],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9802829,0.00004328247,0.01577028,0.0007826467,0.0001678209,0.001168238,0.0003584212,0.00004682281,0.001379558],"genre_scores_gemma":[0.9936407,0.0008068386,0.003913517,0.00009048716,0.00003370254,0.0001418173,0.001175184,0.00001151606,0.0001862462],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2018816,"threshold_uncertainty_score":0.4081223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04828953429970603,"score_gpt":0.3674036867489044,"score_spread":0.3191141524491984,"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."}}