{"id":"W3152604983","doi":"10.1161/circgen.120.003259","title":"Prediction of Genotype Positivity in Patients With Hypertrophic Cardiomyopathy Using Machine Learning","year":2021,"lang":"en","type":"article","venue":"Circulation Genomic and Precision Medicine","topic":"Cardiomyopathy and Myosin Studies","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; National Heart, Lung, and Blood Institute; National Institute on Aging","keywords":"Hypertrophic cardiomyopathy; Receiver operating characteristic; Medicine; Internal medicine; Genotype; Test set; Random forest; Machine learning; Artificial intelligence; Computer science; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002413194,0.0005181835,0.000368322,0.0008683843,0.0001868661,0.0005372753,0.0003065329,0.0004291228,0.0006135093],"category_scores_gemma":[0.006394017,0.0001421038,0.0004554351,0.0003325488,0.0002132522,0.0002808374,0.0003329964,0.0005437253,0.0002439112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004520708,"about_ca_system_score_gemma":0.0005365387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003366202,"about_ca_topic_score_gemma":0.004094419,"domain_scores_codex":[0.9993467,0.000338794,0.0000476855,0.0001106254,0.00009224402,0.00006403384],"domain_scores_gemma":[0.9964057,0.002450205,0.0005067713,0.0001568842,0.0003259109,0.0001545124],"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":[0.0002984974,0.0001229158,0.946396,0.00001640845,0.0001109959,0.00010699,0.00005672802,0.03016387,0.0004723237,0.00005878037,0.0004751503,0.02172128],"study_design_scores_gemma":[0.0000427034,0.0004607057,0.3085421,0.00003565108,0.0001032067,0.0004164672,0.00008807946,0.6882506,0.0009388935,0.0007971664,0.0003030209,0.00002142716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9902201,0.0002032414,0.008691672,0.0002229863,0.0000136731,0.00001953082,0.0001607742,0.00005908037,0.0004089062],"genre_scores_gemma":[0.997401,0.00003751016,0.002255408,0.00002179316,0.00001109892,0.000008332582,0.000182874,0.000003352126,0.00007868854],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003366202,"threshold_uncertainty_score":0.01276237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02509361549243395,"score_gpt":0.237460489945717,"score_spread":0.2123668744532831,"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."}}