{"id":"W1974230338","doi":"10.1109/ccece.2013.6567775","title":"Virtual cardiologist — A conversational system for medical diagnosis","year":2013,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Conversation; Computer science; Focus (optics); Meaning (existential); Process (computing); Medical diagnosis; Ask price; Human–computer interaction; Multimedia; Psychology; Programming language; Medicine; Radiology","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":[0.0003694523,0.00008279862,0.000146278,0.00003767871,0.00007436612,0.00009183968,0.0006110419,0.00009125401,0.0002040502],"category_scores_gemma":[0.0002278311,0.00006468952,0.0000767301,0.00005997827,0.00004052378,0.0002317187,0.0001669228,0.00006175085,0.0003750461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006031783,"about_ca_system_score_gemma":0.00009740808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003043475,"about_ca_topic_score_gemma":0.0000193718,"domain_scores_codex":[0.9988903,0.00004373142,0.0002137105,0.0003012746,0.0003468071,0.0002041984],"domain_scores_gemma":[0.9989136,0.0004554681,0.00003736632,0.0003446991,0.000112547,0.0001363121],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002190082,0.00002963043,0.006760532,0.00003976271,0.00005543344,0.000001978889,0.0002913432,0.0005740715,0.00004902001,0.9142995,0.03876822,0.03912829],"study_design_scores_gemma":[0.001151943,0.0001137261,0.002876391,0.000063459,0.00001280156,0.00006353175,0.0002723612,0.9296521,0.0002936054,0.003534182,0.06152213,0.0004437701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01369853,0.00002611907,0.9796159,0.003748808,0.0006689309,0.0002756387,0.000002293543,0.0002258023,0.001737972],"genre_scores_gemma":[0.8987614,0.000003328199,0.09944098,0.0006417567,0.0002393663,0.0004102658,0.000004604725,0.000004480314,0.0004938192],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.929078,"threshold_uncertainty_score":0.4820583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03184602320474247,"score_gpt":0.2570850538251836,"score_spread":0.2252390306204411,"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."}}