{"id":"W4403064496","doi":"10.1038/s41746-024-01260-z","title":"Improving prognostic accuracy in lung transplantation using unique features of isolated human lung radiographs","year":2024,"lang":"en","type":"article","venue":"npj Digital Medicine","topic":"Transplantation: Methods and Outcomes","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Toronto General Hospital; Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"University of Toronto; University Health Network Foundation; University Health Network","keywords":"Lung; Lung transplantation; Radiography; Medicine; Human lung; Transplantation; Radiology; Pathology; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001726366,0.0005626946,0.0005150079,0.001593743,0.0001310439,0.000925332,0.0003888054,0.0004138969,0.001118235],"category_scores_gemma":[0.005205587,0.000213558,0.0003775355,0.0006109107,0.0002627145,0.0005108429,0.0007747185,0.0004854706,0.0002924466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002986612,"about_ca_system_score_gemma":0.000307359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001524177,"about_ca_topic_score_gemma":0.002976685,"domain_scores_codex":[0.9996159,0.0001255487,0.00003302436,0.00009121042,0.00007200395,0.00006231428],"domain_scores_gemma":[0.9986093,0.0006130187,0.0003492646,0.0001346272,0.0001844602,0.0001092796],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009264543,0.0001262534,0.7908202,0.0001207315,0.000217424,0.0003482017,0.0001074643,0.01101371,0.01869583,0.0002257969,0.001418484,0.1759794],"study_design_scores_gemma":[0.00004765442,0.0004450791,0.7294198,0.0001212014,0.0002956224,0.001547061,0.0002272245,0.2458185,0.01818061,0.001504241,0.002339225,0.00005381512],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9827001,0.001102151,0.01434575,0.0002265269,0.00003983905,0.00002553992,0.0005534866,0.0001864645,0.0008201081],"genre_scores_gemma":[0.9952885,0.0001989253,0.003766188,0.00002246653,0.00002604102,0.000009600724,0.0004898018,0.00001025643,0.000188278],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001726366,"threshold_uncertainty_score":0.009129941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02056182259663498,"score_gpt":0.3480459396070911,"score_spread":0.3274841170104561,"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."}}