{"id":"W4384120699","doi":"10.1177/08465371231180844","title":"Machine Learning Classification of Body Part, Imaging Axis, and Intravenous Contrast Enhancement on CT Imaging","year":2023,"lang":"en","type":"article","venue":"Canadian Association of Radiologists Journal","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital; North York General Hospital; Public Health Ontario; University of Toronto","funders":"","keywords":"Medicine; Generalizability theory; Contrast (vision); Intravenous contrast; Computed tomography; Artificial intelligence; Radiology; Nuclear medicine; Computer science; Statistics","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":[0.0007865455,0.0001288015,0.0002392965,0.0003138678,0.000171492,0.00003594133,0.00009869913,0.00003028662,0.00003101339],"category_scores_gemma":[0.0003739611,0.0001380253,0.00004955353,0.0001944046,0.00004892698,0.0001463777,0.000007091305,0.0003863796,0.000006249144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000737129,"about_ca_system_score_gemma":0.00008633158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003566762,"about_ca_topic_score_gemma":0.0005559042,"domain_scores_codex":[0.9988507,0.00008814895,0.0003949836,0.000123006,0.0001689353,0.0003742337],"domain_scores_gemma":[0.9991807,0.0001546999,0.0003122763,0.00008015961,0.00009953261,0.0001726275],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003262005,0.00003990529,0.6280361,0.0001381398,0.0003625717,0.0001976712,0.001484294,0.05906674,0.04160409,0.00216269,0.00860519,0.2582699],"study_design_scores_gemma":[0.002785597,0.0001846215,0.5257587,0.0004801088,0.0001248453,0.0003674996,0.002152174,0.4018089,0.006435366,0.001670722,0.05737812,0.0008533711],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9618675,0.004346531,0.01937596,0.003444698,0.001864044,0.0004049757,0.0001620806,0.0002828649,0.008251377],"genre_scores_gemma":[0.9981562,0.001331851,0.0001379563,0.00005661685,0.0001192243,0.00000416339,0.00003994065,0.00002031079,0.0001337103],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3427421,"threshold_uncertainty_score":0.562851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008254692972358292,"score_gpt":0.2219062840808491,"score_spread":0.2136515911084908,"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."}}