{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003784935,0.0001341629,0.0004979876,0.0001775119,0.00008838831,0.000004234492,0.00002051385,0.00007326552,0.00003222053],"category_scores_gemma":[0.0001857568,0.0001047591,0.00004681613,0.0003033082,0.00009848554,0.0000621057,0.00004456479,0.0001718225,8.23985e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008657729,"about_ca_system_score_gemma":0.0000650185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007897757,"about_ca_topic_score_gemma":0.000005137197,"domain_scores_codex":[0.9988153,0.0001226651,0.0003445153,0.0002841353,0.0003024721,0.0001309311],"domain_scores_gemma":[0.9992559,0.00009184298,0.0001179447,0.0001777049,0.0002827305,0.00007386139],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004738584,0.00004998858,0.9537913,0.00007065852,0.00006629168,0.00002144338,0.0005871611,0.0009574351,0.03328958,0.00001514913,0.000003935788,0.01067315],"study_design_scores_gemma":[0.004243954,0.0003208902,0.9907067,0.0003374687,0.000245003,0.00008819815,0.0001521369,0.003275061,0.0002247687,0.00004456559,0.0002804214,0.00008087156],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9916837,0.004812607,0.002616095,0.00009173765,0.0001491514,0.0002763046,0.00001478717,0.00001707698,0.0003385037],"genre_scores_gemma":[0.9988114,0.0003238623,0.0005136755,0.00005265497,0.0001244983,0.000002921322,0.0001236124,0.0000162756,0.00003114308],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03691531,"threshold_uncertainty_score":0.4271953,"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."}}