{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001986775,0.0008539346,0.0004278031,0.001763937,0.0004884274,0.002839618,0.0009515841,0.001615024,0.007828053],"category_scores_gemma":[0.003999412,0.0007712212,0.000356898,0.001231766,0.0006210834,0.001108244,0.001130766,0.001714856,0.002869234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008966796,"about_ca_system_score_gemma":0.001907385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004033935,"about_ca_topic_score_gemma":0.003338796,"domain_scores_codex":[0.9993548,0.0002708748,0.00004186888,0.00008752877,0.0001938449,0.00005118784],"domain_scores_gemma":[0.998769,0.000556156,0.000132741,0.000107211,0.0003066997,0.0001281268],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002767802,0.0003395669,0.01717793,0.003040861,0.0002858034,0.002564184,0.0005036871,0.007150462,0.5095772,0.0165251,0.04888189,0.3911856],"study_design_scores_gemma":[0.0006710665,0.001714284,0.02601204,0.001656252,0.0006980493,0.02853787,0.0004922886,0.1491801,0.4272954,0.01053808,0.3527591,0.000445377],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07597166,0.04591968,0.7768075,0.008544156,0.00090844,0.001283836,0.002157681,0.009116561,0.07929049],"genre_scores_gemma":[0.304617,0.01301279,0.6601982,0.004732928,0.0004740205,0.0009754504,0.001661764,0.002367761,0.01196005],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007828053,"threshold_uncertainty_score":0.02618748,"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."}}