{"id":"W4408786031","doi":"10.1016/j.cjco.2025.03.013","title":"Echoing Intelligence: Assessing the Capabilities of a Novel Generative AI With a Large Language Model in Cardiology Education","year":2025,"lang":"en","type":"article","venue":"CJC Open","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"North York General Hospital; University of Toronto","funders":"","keywords":"Generative grammar; Internal medicine; Cardiology; Computer science; Medicine; 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":[],"consensus_categories":[],"category_scores_codex":[0.0005477755,0.00007500652,0.0002492251,0.0001030762,0.00009047956,0.00004045707,0.0001604317,0.000062498,0.00001608834],"category_scores_gemma":[0.0002634111,0.00004974538,0.00002404494,0.0003032301,0.00008784301,0.0001632078,0.00006257068,0.0001927146,0.000001782912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000131421,"about_ca_system_score_gemma":0.001923447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003278003,"about_ca_topic_score_gemma":0.00107001,"domain_scores_codex":[0.9992476,0.00006775506,0.0002819895,0.0001757316,0.00007522152,0.0001517433],"domain_scores_gemma":[0.999281,0.0001540943,0.00006477125,0.000261471,0.0002122869,0.0000263563],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0006033127,0.002062725,0.37847,0.001113052,0.0002519795,0.00001109365,0.2430577,0.0310913,0.01437094,0.09518808,0.003685674,0.2300942],"study_design_scores_gemma":[0.0004007915,0.0003746193,0.02307698,0.002606685,0.0001769219,0.00005360235,0.6325094,0.2543159,0.06749433,0.01760214,0.001010752,0.0003779372],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9148199,0.0003534309,0.0678776,0.005080076,0.0001794165,0.0008880251,0.000004383505,0.000008404251,0.01078878],"genre_scores_gemma":[0.9931306,0.00001973997,0.003658485,0.00184637,0.00007393742,0.0001530842,0.00001676361,0.000006481254,0.001094508],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3894517,"threshold_uncertainty_score":0.4955382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1184488787254064,"score_gpt":0.4969808349971815,"score_spread":0.3785319562717751,"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."}}