{"id":"W4398132461","doi":"10.1016/j.resinv.2024.05.010","title":"Computed tomography machine learning classifier correlates with mortality in interstitial lung disease","year":2024,"lang":"en","type":"article","venue":"Respiratory Investigation","topic":"Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Medicine; Interstitial lung disease; Computed tomography; Classifier (UML); Lung; Radiology; Artificial intelligence; Internal medicine","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":[0.000218074,0.0002039219,0.0002254073,0.0003489046,0.00008211334,0.00007858518,0.00006437866,0.00009135299,0.00007594318],"category_scores_gemma":[0.00005557495,0.0001700471,0.0001235962,0.0005648957,0.0002212599,0.0002692667,0.00003113901,0.0004806843,0.00002283222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001628613,"about_ca_system_score_gemma":0.0003020459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007152488,"about_ca_topic_score_gemma":0.00002982583,"domain_scores_codex":[0.9986045,0.0001349215,0.0003216927,0.0004014779,0.0003082957,0.0002290547],"domain_scores_gemma":[0.9992958,0.00005133771,0.00005678417,0.0001867174,0.00007027926,0.0003390608],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001694389,0.00009880801,0.9873586,0.001484794,0.0001399954,0.001145437,0.0003412211,0.0006885907,0.003359422,0.002302455,0.0005706336,0.0008156276],"study_design_scores_gemma":[0.001328913,0.001851555,0.5496233,0.006059462,0.0005889522,0.00003201679,0.0001198309,0.4327269,0.0005287968,0.0005560912,0.006066987,0.000517168],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913277,0.00552629,0.0009734284,0.0004317888,0.0003250832,0.0004298677,0.00004087832,0.000288171,0.0006568025],"genre_scores_gemma":[0.998523,0.00001615122,0.00006174461,0.0007527902,0.0002549631,0.0000401327,0.00022564,0.00003555937,0.00009002284],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4377353,"threshold_uncertainty_score":0.6934318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02094478957761099,"score_gpt":0.2695827865545795,"score_spread":0.2486379969769685,"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."}}