{"id":"W4210371455","doi":"10.2196/30483","title":"Disease Progression of Hypertrophic Cardiomyopathy: Modeling Using Machine Learning","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Cardiomyopathy and Myosin Studies","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Commission","keywords":"Hypertrophic cardiomyopathy; Medicine; Internal medicine; Cardiology; Machine learning; Ejection fraction; Heart disease; Cardiomyopathy; Sudden cardiac death; Heart failure; Artificial intelligence; Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.0006155603,0.0001541511,0.0004848762,0.0001619865,0.0003143393,0.000008735484,0.0001200012,0.00005648747,0.0001227403],"category_scores_gemma":[0.0002537155,0.0001251168,0.0002190107,0.0003118424,0.0001030644,0.00009904891,0.0004764268,0.0006397616,0.000003319079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000961084,"about_ca_system_score_gemma":0.0003071405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001587973,"about_ca_topic_score_gemma":2.1843e-7,"domain_scores_codex":[0.9974567,0.00007701819,0.0006235474,0.0001025261,0.001476939,0.0002632631],"domain_scores_gemma":[0.9991027,0.00004271928,0.0001717171,0.000237139,0.0001082109,0.000337506],"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":[0.008279329,0.003343752,0.3866006,0.01537892,0.00220111,0.003655321,0.07392793,0.2096333,0.003411144,0.001963262,0.00195253,0.2896528],"study_design_scores_gemma":[0.001503456,0.000296785,0.0003386198,0.0003236558,0.0002118235,0.0003277391,0.003462285,0.9869998,0.00004025219,0.00003233991,0.006301704,0.0001615965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932771,0.001912027,0.002880943,0.0002231561,0.0002151951,0.0004479882,0.00002019425,0.00009360165,0.0009297545],"genre_scores_gemma":[0.9977099,0.000153372,0.001448625,0.0003246129,0.000149745,0.00008216988,0.00006424085,0.00002018443,0.00004709774],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7773665,"threshold_uncertainty_score":0.5102113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02852060031612962,"score_gpt":0.3149987947326067,"score_spread":0.286478194416477,"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."}}