{"id":"W4407872939","doi":"10.2196/68066","title":"Machine Learning Model for Predicting Coronary Heart Disease Risk: Development and Validation Using Insights From a Japanese Population–Based Study","year":2025,"lang":"en","type":"article","venue":"JMIR Cardio","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Medicine; Computer science; World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01188868,0.0008938471,0.001007235,0.00159768,0.0005685323,0.0007033874,0.0009968617,0.0006275552,0.0008059922],"category_scores_gemma":[0.01560325,0.000240156,0.001248756,0.0009314775,0.0003898249,0.0005799179,0.0006884299,0.0007152341,0.0002032825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006613916,"about_ca_system_score_gemma":0.001305197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01885323,"about_ca_topic_score_gemma":0.008476483,"domain_scores_codex":[0.9981632,0.001224491,0.000130018,0.0002349652,0.000167357,0.00007999176],"domain_scores_gemma":[0.9937184,0.004153053,0.0002879922,0.0004343078,0.001244936,0.000161333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008400028,0.001075979,0.8261211,0.000159581,0.0009682417,0.0005598934,0.001084257,0.09361785,0.0009563107,0.0007934145,0.001647492,0.07217588],"study_design_scores_gemma":[0.0001418357,0.0006478442,0.1752275,0.00005673149,0.0004949226,0.0002211842,0.0005209257,0.8208814,0.0004049845,0.0007475966,0.0006157819,0.0000392276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9713573,0.0003512758,0.02714794,0.0001374244,0.00002518806,0.0001878057,0.0002760239,0.00005806853,0.0004588881],"genre_scores_gemma":[0.9848252,0.0001697731,0.01397664,0.00003515972,0.00001747621,0.0002041578,0.0005941872,0.000008975125,0.0001685058],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01885323,"threshold_uncertainty_score":0.06287414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1365624066184328,"score_gpt":0.4504471927604465,"score_spread":0.3138847861420138,"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."}}