{"id":"W4409782851","doi":"10.1016/j.healun.2025.02.1444","title":"Validation of a Machine Learning Model That Predicts Donor Organ Suitability during Ex Vivo Lung Perfusion","year":2025,"lang":"en","type":"article","venue":"The Journal of Heart and Lung Transplantation","topic":"Transplantation: Methods and Outcomes","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Vector Institute; University Health Network","funders":"","keywords":"Ex vivo; Perfusion; Lung; Machine perfusion; In vivo; Medicine; Computer science; Internal medicine; Biology; Transplantation","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":[0.003997644,0.0009298734,0.0007519352,0.0006484965,0.0003130181,0.001156346,0.0008207392,0.001486225,0.001087029],"category_scores_gemma":[0.005452188,0.0002878126,0.000682726,0.0003140641,0.0004298138,0.0005899314,0.0005596463,0.0009830395,0.0005310843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006098331,"about_ca_system_score_gemma":0.00100612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003566042,"about_ca_topic_score_gemma":0.002150811,"domain_scores_codex":[0.9993904,0.0002075728,0.00006365974,0.0001775611,0.0000943972,0.0000665538],"domain_scores_gemma":[0.9962828,0.002457574,0.0002268475,0.0002696042,0.0006517864,0.0001114565],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.002749199,0.002237623,0.1184499,0.0002131861,0.0007727699,0.0002986352,0.0001207406,0.7057697,0.03248687,0.0004335799,0.003710129,0.1327575],"study_design_scores_gemma":[0.00003213562,0.0002672915,0.006809238,0.00001087476,0.00004446458,0.00005032677,0.00001708754,0.9865428,0.005922664,0.0001392799,0.0001525954,0.00001125614],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9412572,0.0003897872,0.055519,0.0002779824,0.0001411305,0.00009428975,0.0008565421,0.0006339857,0.0008300969],"genre_scores_gemma":[0.990096,0.00006237492,0.007804929,0.00006191833,0.00001926509,0.00005861839,0.001383183,0.00002618668,0.0004876051],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003997644,"threshold_uncertainty_score":0.02114177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01546425663952269,"score_gpt":0.2977864658255513,"score_spread":0.2823222091860287,"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."}}