{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001808166,0.0006636376,0.0004118021,0.0006645455,0.0003067477,0.0005321971,0.0006615565,0.000690607,0.000684935],"category_scores_gemma":[0.00530953,0.0001805685,0.0006141353,0.0003996449,0.0003242037,0.0004088507,0.0004117865,0.0007932184,0.0001496532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005685136,"about_ca_system_score_gemma":0.0005609049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01031745,"about_ca_topic_score_gemma":0.005828919,"domain_scores_codex":[0.9994687,0.0002978934,0.00002371656,0.00009841759,0.00007193776,0.00003931187],"domain_scores_gemma":[0.9958203,0.003470823,0.0002875728,0.0001177763,0.0002408413,0.00006270498],"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":[0.00003372423,0.00005047017,0.007020076,0.00002353949,0.00002668397,0.00004133434,0.00004123563,0.9755846,0.0002446186,0.0009223633,0.0002538086,0.01575762],"study_design_scores_gemma":[0.000001483845,0.000007633615,0.0003756795,0.000002560745,0.000002399299,0.000006497449,0.000002559832,0.9987397,0.0000580082,0.0007576436,0.00004409405,0.000001801335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3623977,0.00102637,0.6313303,0.001116839,0.0000656492,0.0001687793,0.0005378916,0.00082898,0.002527352],"genre_scores_gemma":[0.9570271,0.0002090225,0.04169275,0.00007112163,0.00004323436,0.0001148628,0.0002355064,0.00001521335,0.0005910259],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01031745,"threshold_uncertainty_score":0.02051479,"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."}}