{"id":"W4391447087","doi":"10.1002/ima.23028","title":"Differentiation of COVID‐19 pneumonia from other lung diseases using CT radiomic features and machine learning: A large multicentric cohort study","year":2024,"lang":"en","type":"article","venue":"International Journal of Imaging Systems and Technology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Coronavirus disease 2019 (COVID-19); Pneumonia; 2019-20 coronavirus outbreak; Cohort; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Medicine; Lung; Betacoronavirus; Radiomics; Cohort study; Artificial intelligence; Machine learning; Computer science; Medical physics; Radiology; Pathology; Internal medicine; Infectious disease (medical specialty)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002165798,0.0005542249,0.0005484557,0.0008823755,0.0004800775,0.0007282704,0.0005762554,0.0004975596,0.001175335],"category_scores_gemma":[0.003515138,0.0004380122,0.0008509682,0.000693653,0.0003273439,0.0005386575,0.0006889032,0.0005497422,0.0003567646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003364062,"about_ca_system_score_gemma":0.0002882666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002684216,"about_ca_topic_score_gemma":0.00264373,"domain_scores_codex":[0.9990333,0.0002840471,0.00009510112,0.0003759228,0.0001221068,0.00008954165],"domain_scores_gemma":[0.9981363,0.000397861,0.000394234,0.0006693501,0.0002458248,0.0001564186],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003424145,0.00006958772,0.9946578,0.00001124485,0.0001801786,0.0002784486,0.00007320072,0.0001962262,0.001179804,0.00003479735,0.000228884,0.002747485],"study_design_scores_gemma":[0.00004611816,0.0003535451,0.9936675,0.00001432691,0.0001429081,0.001504294,0.0003019308,0.002520244,0.0006273694,0.00007057306,0.0007338711,0.0000173023],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983155,0.000125497,0.0008932388,0.00001999964,0.000005444,0.00002488285,0.0004735611,0.00000475241,0.0001371099],"genre_scores_gemma":[0.9979885,0.00007087961,0.0006479665,0.00002223617,0.00001263472,0.00002333651,0.001109094,0.00000877152,0.0001166453],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002684216,"threshold_uncertainty_score":0.01145393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006899129293915221,"score_gpt":0.3003786153671597,"score_spread":0.2934794860732445,"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."}}