{"id":"W3011311220","doi":"10.2196/16975","title":"Minimal Patient Clinical Variables to Accurately Predict Stress Echocardiography Outcome: Validation Study Using Machine Learning Techniques","year":2020,"lang":"en","type":"article","venue":"JMIR Cardio","topic":"Cardiovascular Function and Risk Factors","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council","keywords":"Random forest; Coronary artery disease; Feature selection; Medicine; Stress Echocardiography; Machine learning; CAD; Chest pain; Outcome (game theory); Artificial intelligence; Anthropometry; Support vector machine; Internal medicine; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007479715,0.0003170547,0.001012838,0.0002536794,0.0001631132,0.00007793182,0.0001194203,0.0001941447,0.00004546728],"category_scores_gemma":[0.0003795823,0.0002637657,0.001286467,0.0007908089,0.00004460472,0.0001272227,0.0001711555,0.0006481212,0.0000263387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008614553,"about_ca_system_score_gemma":0.00007846477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001333448,"about_ca_topic_score_gemma":0.000002217604,"domain_scores_codex":[0.9967909,0.0005722626,0.0008290174,0.0006733626,0.0008189136,0.0003155122],"domain_scores_gemma":[0.9985712,0.00008622032,0.0001620557,0.0004704694,0.0002272444,0.0004828065],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004935673,0.0002323557,0.9849091,0.00005431195,0.001233397,0.0001144175,0.000757236,0.0007592319,0.0004398846,0.000001092077,0.0005795743,0.01042589],"study_design_scores_gemma":[0.003373449,0.004911632,0.8866634,0.0001397707,0.002136703,0.0000751933,0.003469565,0.001221901,0.005375582,0.000001304781,0.09194171,0.0006897909],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9903159,0.0001210815,0.005178861,0.0001798116,0.0005441887,0.002511013,0.00006013631,0.0005599574,0.0005291111],"genre_scores_gemma":[0.9964225,0.00004777114,0.001947232,0.0004360581,0.0008511641,0.0001150878,0.00008945379,0.00006275951,0.00002799096],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09824564,"threshold_uncertainty_score":0.9999815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08877411517667101,"score_gpt":0.3706411233171512,"score_spread":0.2818670081404802,"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."}}