{"id":"W3135370757","doi":"10.1016/j.clinimag.2021.02.017","title":"QIBA guidance: Computed tomography imaging for COVID-19 quantitative imaging applications","year":2021,"lang":"en","type":"article","venue":"Clinical Imaging","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Vancouver General Hospital; University of British Columbia","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; U.S. National Library of Medicine; Peking Union Medical College; National Institute of Environmental Health Sciences; Chinese Academy of Medical Sciences; Women's College Hospital; Universidad de Navarra; Hebrew University of Jerusalem; Sun Yat-sen University; Sveučilište u Zagrebu","keywords":"Medicine; Medical imaging; Coronavirus disease 2019 (COVID-19); Tomography; Medical physics; Computed tomography; Radiology; Imaging science; Radiological weapon; Radiological imaging; Pathology; Disease; Infectious disease (medical specialty)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001752423,0.0003722883,0.0008184075,0.000331402,0.0004985456,0.0002059909,0.0003328927,0.00007541725,0.00009372256],"category_scores_gemma":[0.007139328,0.0004032527,0.000708508,0.001122462,0.0006756706,0.0002737969,0.0002812389,0.0005815238,0.00006621076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000290997,"about_ca_system_score_gemma":0.001269834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006898207,"about_ca_topic_score_gemma":0.00001024744,"domain_scores_codex":[0.995895,0.0002707102,0.001377737,0.001352234,0.000396938,0.0007073968],"domain_scores_gemma":[0.989692,0.007024001,0.0004112748,0.001064317,0.001034935,0.0007734877],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002154452,0.00135699,0.7063506,0.0009071252,0.0002364858,0.0004696438,0.0004528188,0.0001661279,0.003101507,0.004434209,0.2103928,0.07191621],"study_design_scores_gemma":[0.007704154,0.00007653527,0.0553325,0.0007781734,0.0007747014,0.0002364937,0.001259679,0.09009507,0.0009219113,0.008529519,0.8334181,0.0008732047],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"commentary","genre_scores_codex":[0.001900123,0.00741292,0.7139784,0.2729128,0.0008480437,0.001581622,0.0001009639,0.000753137,0.0005120388],"genre_scores_gemma":[0.3175897,0.0001841762,0.2167936,0.4619239,0.001552229,0.0009061642,0.0005714722,0.0002156759,0.00026314],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6510181,"threshold_uncertainty_score":0.9998419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1051485244924584,"score_gpt":0.4838431174101057,"score_spread":0.3786945929176473,"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."}}