{"id":"W4402802218","doi":"10.1177/08465371241280219","title":"Development and Evaluation of an Automated Protocol Recommendation System for Chest CT Using Natural Language Processing With CLEVER Terminology Word Replacement","year":2024,"lang":"en","type":"article","venue":"Canadian Association of Radiologists Journal","topic":"Radiology practices and education","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Medicine; Terminology; Protocol (science); Kappa; Cohen's kappa; Medical physics; Natural language processing; Radiology; Artificial intelligence; Computer science; Machine learning; Pathology; Linguistics","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":[0.002703258,0.00009025192,0.0002151593,0.0002715015,0.0001321479,0.00004070826,0.00003714472,0.00008367789,0.00001293245],"category_scores_gemma":[0.0003309546,0.00007175673,0.00002352873,0.0001250305,0.00003158908,0.0002532004,0.000003176948,0.000163319,2.723459e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002852017,"about_ca_system_score_gemma":0.002800485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004373684,"about_ca_topic_score_gemma":0.001681347,"domain_scores_codex":[0.9989059,0.0001960976,0.0003923147,0.0001570771,0.0001658028,0.0001828138],"domain_scores_gemma":[0.9987081,0.00007527949,0.0006071912,0.00006216516,0.0004343432,0.0001129229],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008947738,0.0001157578,0.07373568,0.002142641,0.0008971056,0.0000451158,0.008474184,0.0004533232,0.005193696,0.0000601421,0.001636778,0.9063508],"study_design_scores_gemma":[0.005919496,0.0009207401,0.323011,0.001469705,0.000946912,0.005376903,0.008794671,0.6372555,0.002760126,0.00001500175,0.01319288,0.0003370381],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9924591,0.0001324616,0.0001530742,0.0006800257,0.0002369764,0.006208235,0.000007542028,0.00003369462,0.00008893877],"genre_scores_gemma":[0.9865476,0.000001717678,0.01224723,0.00003543358,0.0001280432,0.0008935585,0.00008555596,0.00001133721,0.00004953964],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9060138,"threshold_uncertainty_score":0.7457924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05680272163708174,"score_gpt":0.3974452802467827,"score_spread":0.3406425586097009,"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."}}