{"id":"W4311456908","doi":"10.1016/j.jacadv.2022.100153","title":"Artificial Intelligence in Congenital Heart Disease","year":2022,"lang":"en","type":"article","venue":"JACC Advances","topic":"Congenital Heart Disease Studies","field":"Medicine","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Heart disease; Leverage (statistics); Disease; Medicine; Software deployment; Intensive care medicine; Artificial intelligence; Cardiology; Computer science; Internal medicine","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":[0.000108645,0.0001228144,0.0002169884,0.0001305928,0.000176559,0.000009924831,0.00007413897,0.00001109285,0.0008511247],"category_scores_gemma":[0.0001595279,0.0001228783,0.0000841803,0.0003710359,0.000105813,0.0001389027,0.000176478,0.0001990188,0.00008699897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001068705,"about_ca_system_score_gemma":0.0001179388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001562315,"about_ca_topic_score_gemma":0.00006479028,"domain_scores_codex":[0.9988014,0.00004841711,0.0002573704,0.0002915972,0.0003341596,0.0002670817],"domain_scores_gemma":[0.9994711,0.00009322289,0.00002375892,0.0002008141,0.00003735266,0.0001737241],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.005203151,0.002003661,0.3833824,0.0002481732,0.00008626298,0.001826151,0.001452364,0.0004092224,0.001052988,0.002514216,0.001788893,0.6000325],"study_design_scores_gemma":[0.001571011,0.002260826,0.6024563,0.0002032353,0.0004497097,0.0002526858,0.02529611,0.008456565,0.001838419,0.1109459,0.2443955,0.001873723],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9762465,0.01663988,0.00007234606,0.003094606,0.0004781787,0.0005968311,0.00008653863,0.0000996411,0.002685524],"genre_scores_gemma":[0.9979786,0.0000609761,0.0002787458,0.0007804601,0.0001363301,0.0002230748,0.00001396641,0.00001546514,0.0005123442],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5981588,"threshold_uncertainty_score":0.9319218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02658361365058279,"score_gpt":0.3272684997954395,"score_spread":0.3006848861448567,"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."}}