{"id":"W4221117972","doi":"10.1016/j.acra.2022.03.025","title":"A Soft Labeling Approach to Develop Automated Algorithms that Incorporate Uncertainty in Pulmonary Opacification on Chest CT using COVID-19 Pneumonia","year":2022,"lang":"en","type":"article","venue":"Academic Radiology","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia Hospital; St. Paul's Hospital; McGill University; University of British Columbia","funders":"","keywords":"Ground truth; Computer science; Artificial intelligence; Ground-glass opacity; Coronavirus disease 2019 (COVID-19); Pattern recognition (psychology); Medicine; Pathology","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":[0.002266999,0.0003569199,0.0007057984,0.001570679,0.0003473592,0.00001179118,0.0004647132,0.0002774364,0.00002555846],"category_scores_gemma":[0.002113249,0.0003748045,0.00006123802,0.003656479,0.0001488356,0.00008953746,0.0003012074,0.001681438,0.00002567463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003247039,"about_ca_system_score_gemma":0.001616128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001218089,"about_ca_topic_score_gemma":0.000007636651,"domain_scores_codex":[0.9963661,0.0007423112,0.0007188129,0.001057416,0.0004222688,0.0006930326],"domain_scores_gemma":[0.9977732,0.000893801,0.0003484612,0.0005054055,0.00008264151,0.0003965278],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008489869,0.000587052,0.09601337,0.0003173465,0.0001244887,0.0008670062,0.004282752,0.7801126,0.09747835,0.0004124885,0.01118984,0.007765737],"study_design_scores_gemma":[0.002610119,0.0004116576,0.03981545,0.0001637841,0.0001260673,0.005133432,0.001629128,0.8580099,0.0007749923,0.0005138068,0.08995698,0.0008546461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9495697,0.000503008,0.001793344,0.04518828,0.000435132,0.001569135,0.00005051998,0.000777472,0.0001133779],"genre_scores_gemma":[0.945357,0.0001592955,0.00545605,0.04783626,0.0001680723,0.0005008714,0.0003836613,0.00007653151,0.00006226195],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09670337,"threshold_uncertainty_score":0.9998704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1037183103061923,"score_gpt":0.3608057104940076,"score_spread":0.2570874001878153,"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."}}