{"id":"W3153999869","doi":"10.1111/ajt.16616","title":"Predicting donor lung acceptance for transplant during ex vivo lung perfusion: The EX vivo lung PerfusIon pREdiction (EXPIRE)","year":2021,"lang":"en","type":"article","venue":"American Journal of Transplantation","topic":"Transplantation: Methods and Outcomes","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University Health Network","funders":"","keywords":"Medicine; Cohort; Lung; Ex vivo; Lung transplantation; Logistic regression; Transplantation; Discriminative model; Internal medicine; Surgery; In vivo; Machine learning","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008808664,0.0003881383,0.0007687464,0.0002522952,0.0005386815,0.00009240674,0.0002364349,0.0001257647,0.0002497018],"category_scores_gemma":[0.00006594221,0.0002906704,0.0005297654,0.0005385879,0.0001605688,0.0005597255,0.000009330483,0.0005909918,0.000001312033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001992669,"about_ca_system_score_gemma":0.0003078469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006534501,"about_ca_topic_score_gemma":0.00007234485,"domain_scores_codex":[0.9966162,0.0003594992,0.00124641,0.0004519452,0.0008264193,0.0004994646],"domain_scores_gemma":[0.9974499,0.000721533,0.0007535701,0.0003052214,0.0005090153,0.0002607371],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.008643766,0.0003153723,0.4873265,0.002567143,0.0006502041,0.0009038621,0.02998803,0.001757804,0.4482117,0.0001071257,0.00026248,0.019266],"study_design_scores_gemma":[0.01346656,0.001428447,0.7014341,0.004814044,0.006244928,0.01339075,0.00874547,0.02275372,0.2262394,0.00003729523,0.0006571202,0.0007881756],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8733849,0.001149245,0.1215139,0.001785491,0.0009438561,0.0006549446,0.0002729803,0.00006739758,0.0002272766],"genre_scores_gemma":[0.9749434,0.008099705,0.01499213,0.0003947334,0.001069709,0.00003147829,0.0001249467,0.00006987041,0.0002740202],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2219723,"threshold_uncertainty_score":0.9999545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01150515976914872,"score_gpt":0.2911881714935032,"score_spread":0.2796830117243545,"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."}}