{"id":"W4416528504","doi":"10.1016/j.cjca.2025.11.030","title":"A Custom Artificial Intelligence GPT-4 Model for Pediatric Cardiology: Surpassing the Board Exam Threshold and Transforming Medical Learning","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Cardiology","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; Thinkpath Engineering Services (Canada); University of Toronto","funders":"","keywords":"Applications of artificial intelligence; MEDLINE; On board; Editorial board","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005525824,0.0005171371,0.0003774869,0.0003541062,0.0002641638,0.001172816,0.001649734,0.001248566,0.007079931],"category_scores_gemma":[0.002580165,0.0002677609,0.0006611131,0.0002579021,0.0003497992,0.0009771814,0.0008215929,0.001476337,0.001722375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008881679,"about_ca_system_score_gemma":0.001453768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01408638,"about_ca_topic_score_gemma":0.01145437,"domain_scores_codex":[0.999811,0.0000467593,0.00001214329,0.00005158329,0.00004871796,0.00002980706],"domain_scores_gemma":[0.9993104,0.0002793623,0.0000376872,0.00008289298,0.0002105548,0.0000792389],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004525775,0.0003203764,0.005600307,0.00009022978,0.00007706053,0.0002537245,0.0001056169,0.8654076,0.003696739,0.007655785,0.007888098,0.1084519],"study_design_scores_gemma":[0.00002192379,0.00004966221,0.0003541849,0.000009728709,0.00001689056,0.00003478082,0.0000100173,0.9939784,0.0009794548,0.002292291,0.002245272,0.000007506315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2555923,0.0003882584,0.6986569,0.003099214,0.0006849022,0.0002768984,0.002265015,0.00944664,0.02958993],"genre_scores_gemma":[0.8406545,0.0002193253,0.1436976,0.0005916753,0.00007003462,0.000186873,0.001472005,0.0004364704,0.0126714],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01408638,"threshold_uncertainty_score":0.02800876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1236078571297377,"score_gpt":0.3837194531892062,"score_spread":0.2601115960594685,"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."}}