{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002589186,0.0006994022,0.0007375326,0.0004749575,0.0002197561,0.001511975,0.0006831429,0.0006548155,0.001318852],"category_scores_gemma":[0.004183813,0.0002194172,0.0006503289,0.0002826402,0.0003105483,0.0008752864,0.0009954135,0.001008438,0.000277811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002586843,"about_ca_system_score_gemma":0.0004966444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004761872,"about_ca_topic_score_gemma":0.0006972858,"domain_scores_codex":[0.9992229,0.0003426294,0.00005062221,0.0001505263,0.0001756471,0.00005761862],"domain_scores_gemma":[0.997842,0.0008754723,0.0005524532,0.0002751876,0.0002540551,0.0002008377],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004554479,0.0008538525,0.8072739,0.0002641467,0.0004600609,0.0003595013,0.0001234841,0.008314162,0.0102912,0.00090747,0.002489605,0.1641082],"study_design_scores_gemma":[0.0004008416,0.00708591,0.7866265,0.0001781851,0.001066806,0.004573245,0.0003408383,0.1580286,0.0322417,0.003634577,0.005607319,0.0002154808],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9704576,0.001876569,0.02310887,0.0004388675,0.0001100373,0.0001931504,0.001442693,0.0002134547,0.002158788],"genre_scores_gemma":[0.9850654,0.0003875922,0.01194942,0.0001621017,0.00008975136,0.0001576729,0.001281554,0.00002564672,0.000880964],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002589186,"threshold_uncertainty_score":0.01369303,"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."}}