{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0005209934,0.0001792266,0.0003183303,0.0001532696,0.002132713,0.0000210815,0.00007926884,0.0001261712,0.000008316172],"category_scores_gemma":[0.0005318716,0.0001751657,0.00006072292,0.000176458,0.00001531406,0.0001530895,0.0001077307,0.0004593807,0.000005663856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004081369,"about_ca_system_score_gemma":0.0005779141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004898294,"about_ca_topic_score_gemma":0.00132261,"domain_scores_codex":[0.9975473,0.0007789122,0.0006686352,0.0004500865,0.0002537001,0.0003013889],"domain_scores_gemma":[0.9983168,0.0008273131,0.0002157329,0.0002596509,0.0002231998,0.0001573641],"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":[0.0002482311,0.00005552006,0.9195826,0.0001660756,0.00003499783,0.000001862658,0.01333206,0.06550806,0.00002046329,0.00001862277,0.00001434369,0.001017179],"study_design_scores_gemma":[0.0002794655,0.00002270884,0.4106704,0.0002082077,0.00006046764,4.792192e-8,0.004354984,0.5837776,0.000009157292,0.0004074461,0.0001065907,0.0001028944],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9838367,0.000332448,0.01127571,0.000127861,0.0003305521,0.003838098,0.0000764193,0.000148867,0.00003338676],"genre_scores_gemma":[0.9953055,0.000002045588,0.002884093,0.0002169834,0.0001455134,0.0009864134,0.0002594453,0.00003077721,0.0001691905],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5182695,"threshold_uncertainty_score":0.9991664,"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."}}