{"id":"W4243805521","doi":"10.1016/j.jvir.2009.04.031","title":"The IR Radlex Project: An Interventional Radiology Lexicon—A Collaborative Project of the Radiological Society of North America and the Society of Interventional Radiology","year":2009,"lang":"en","type":"article","venue":"Journal of Vascular and Interventional Radiology","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"Vector Institute","funders":"","keywords":"Subspecialty; Medicine; Terminology; Radiology; Variety (cybernetics); Lexicon; Standardization; Radiology information systems; Medical physics; Computer science; Pathology; Artificial intelligence","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.001939724,0.0002716455,0.0008766521,0.00006935906,0.000216671,0.00001624222,0.0006968855,0.0003388129,0.00001955392],"category_scores_gemma":[0.0005950673,0.0001365658,0.002143057,0.0002429558,0.004849309,0.00002194983,0.0002348741,0.0004913758,1.372272e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003096955,"about_ca_system_score_gemma":0.0003382475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002867788,"about_ca_topic_score_gemma":0.00002679295,"domain_scores_codex":[0.9960473,0.00160993,0.001388447,0.0003510893,0.0002992318,0.0003039792],"domain_scores_gemma":[0.9972072,0.0004244819,0.001470621,0.0003202989,0.0005104509,0.00006689872],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.03256926,0.01308693,0.1624981,0.002095365,0.05199391,0.00003607756,0.01326026,0.001165344,0.1094184,0.06430481,0.309295,0.2402766],"study_design_scores_gemma":[0.0265562,0.05928984,0.7609193,0.0008848118,0.001521814,0.007587376,0.008774082,0.003141061,0.004503089,0.01739668,0.1083094,0.001116371],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9599215,0.02958732,0.006954801,0.002550513,0.0003732087,0.0004433285,0.0001197274,0.000005416584,0.00004418222],"genre_scores_gemma":[0.9901715,0.005439814,0.003564747,0.0002878268,0.0003314616,0.00002113436,0.00007853912,0.00001197048,0.00009300472],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5984213,"threshold_uncertainty_score":0.9978589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01862374785845283,"score_gpt":0.3032958912330144,"score_spread":0.2846721433745616,"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."}}