{"id":"W4224229188","doi":"10.1067/j.cpradiol.2022.04.008","title":"Radiological Lexicon: Use of Disease Severity Modifiers","year":2022,"lang":"en","type":"article","venue":"Current Problems in Diagnostic Radiology","topic":"Radiology practices and education","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Medicine; Lexicon; Grading (engineering); Radiological weapon; Disease; Radiology; Medical physics; Natural language processing; Pathology; Computer science","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":[0.004503821,0.0006426993,0.0009524109,0.008299247,0.0005444254,0.003670268,0.001264387,0.001188831,0.009700951],"category_scores_gemma":[0.04543781,0.0004013221,0.001035888,0.005590723,0.0005256813,0.003121044,0.001801453,0.001459791,0.00526374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001208827,"about_ca_system_score_gemma":0.003276943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007293043,"about_ca_topic_score_gemma":0.007493769,"domain_scores_codex":[0.9964951,0.0007502741,0.001404488,0.0004758304,0.0007472037,0.0001271514],"domain_scores_gemma":[0.9747123,0.01426221,0.00404675,0.001935335,0.004210449,0.0008329886],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001046147,0.0003711884,0.1995687,0.004055266,0.00043365,0.000843715,0.001175736,0.0009246261,0.006864476,0.01166509,0.2397531,0.5332983],"study_design_scores_gemma":[0.0006051852,0.0003966881,0.4140002,0.004017299,0.001769703,0.01092669,0.001748858,0.0133681,0.009869575,0.03381917,0.5090005,0.0004781117],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.2749627,0.03583592,0.1441711,0.06354015,0.004527267,0.002150055,0.325068,0.04019207,0.1095527],"genre_scores_gemma":[0.5597511,0.01927324,0.2301872,0.008833645,0.00194692,0.001046642,0.1632335,0.005816735,0.009911029],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.009700951,"threshold_uncertainty_score":0.03245294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09566678627527014,"score_gpt":0.3390845556114486,"score_spread":0.2434177693361785,"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."}}