{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003644181,0.0008499617,0.0006421644,0.002053983,0.0001939787,0.0009514078,0.000909991,0.0007948122,0.0005680113],"category_scores_gemma":[0.01267648,0.000251951,0.0008332934,0.0008806906,0.000413716,0.000883287,0.0005698824,0.0006579683,0.0004440613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000730566,"about_ca_system_score_gemma":0.0006716202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003351637,"about_ca_topic_score_gemma":0.003897537,"domain_scores_codex":[0.998855,0.000390176,0.0001215747,0.0003101012,0.0002456209,0.00007754483],"domain_scores_gemma":[0.9954391,0.00196854,0.001064213,0.0004544462,0.000939237,0.0001345363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007817407,0.0002931727,0.760304,0.0003478939,0.0004688976,0.0004186979,0.0001610153,0.06392416,0.005713071,0.0003023893,0.003339553,0.1639454],"study_design_scores_gemma":[0.00004513651,0.0005756526,0.2335382,0.0003709478,0.0003526606,0.001591091,0.000240772,0.7465402,0.01154037,0.00164648,0.003491578,0.00006683918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9315224,0.003300905,0.05954762,0.000382144,0.0001101352,0.0001760679,0.002466825,0.0005260607,0.001967778],"genre_scores_gemma":[0.9772319,0.0004875532,0.01789849,0.00008138822,0.00004808307,0.00008125754,0.003675806,0.00003341724,0.0004620872],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003644181,"threshold_uncertainty_score":0.01927251,"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."}}