{"id":"W3183806681","doi":"10.1016/j.healun.2021.07.005","title":"Consensus document for the selection of lung transplant candidates: An update from the International Society for Heart and Lung Transplantation","year":2021,"lang":"en","type":"article","venue":"The Journal of Heart and Lung Transplantation","topic":"Transplantation: Methods and Outcomes","field":"Medicine","cited_by":752,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; SickKids Foundation; University of Toronto","funders":"National Heart, Lung, and Blood Institute; Health Resources and Services Administration; Lung Foundation Netherlands; Boomer Esiason Foundation; ZonMw; Savara Pharmaceuticals; European Respiratory Society; Bristol-Myers Squibb; Astellas Pharma; United Therapeutics Corporation; Cystic Fibrosis Foundation; Galecto; Vertex Pharmaceuticals; National Institutes of Health; U.S. Department of Health and Human Services","keywords":"Lung; Selection (genetic algorithm); Lung transplantation; Transplantation; Medicine; Intensive care medicine; Internal medicine; 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":[0.001371877,0.0001676763,0.0003220545,0.00004417012,0.0003555264,0.00005831591,0.00009644165,0.00008249999,0.00002300876],"category_scores_gemma":[0.00001874184,0.00009214022,0.0002294835,0.00009899912,0.0001051492,0.0001810337,0.000002694504,0.0002181543,1.131203e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003458406,"about_ca_system_score_gemma":0.0001714783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001537445,"about_ca_topic_score_gemma":0.00033576,"domain_scores_codex":[0.9986013,0.0001682112,0.0005710828,0.0001660316,0.0003192798,0.0001740605],"domain_scores_gemma":[0.9970978,0.002228925,0.0002064357,0.0001101153,0.0002835316,0.0000731595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.03331637,0.0003331157,0.2430018,0.003961598,0.005141055,0.00003201433,0.06615418,0.001271428,0.6328907,0.001816609,0.008235456,0.003845621],"study_design_scores_gemma":[0.02059243,0.001079179,0.7207207,0.001835404,0.01926511,0.006872329,0.004878379,0.05265136,0.1667043,0.001216923,0.003670438,0.0005134904],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8978059,0.003107155,0.079646,0.01759496,0.0004488789,0.0008162347,0.0005605284,0.00001257099,0.00000773013],"genre_scores_gemma":[0.9696342,0.01330567,0.01511721,0.001190418,0.0003156777,0.00002275377,0.0003587224,0.00002051948,0.00003484139],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4777189,"threshold_uncertainty_score":0.3757369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01781539634280892,"score_gpt":0.3265217022013659,"score_spread":0.308706305858557,"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."}}