{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009422843,0.0009164565,0.0007416451,0.0008795048,0.0002711872,0.0004285166,0.0006949937,0.0006856903,0.0007697784],"category_scores_gemma":[0.01438676,0.0002056406,0.0007652358,0.000419417,0.0006956347,0.000377087,0.000623758,0.0008011612,0.0003162228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004207988,"about_ca_system_score_gemma":0.0009182186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001307262,"about_ca_topic_score_gemma":0.0007721153,"domain_scores_codex":[0.9977326,0.001325837,0.0001933024,0.0003082702,0.0002791655,0.0001607901],"domain_scores_gemma":[0.9860435,0.01029978,0.0006546723,0.00115694,0.001439163,0.000405987],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004780487,0.005211109,0.8160344,0.000187118,0.0007620372,0.000364611,0.0005757071,0.04293136,0.005383231,0.0002742986,0.001355618,0.12214],"study_design_scores_gemma":[0.0006564325,0.01178782,0.590113,0.00007417496,0.0003752348,0.0007741638,0.0003141036,0.3875759,0.00644277,0.0005956568,0.00122353,0.00006714589],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9907368,0.0001565703,0.008202474,0.00004447681,0.00002586903,0.0002292853,0.0002847011,0.00005924891,0.0002606653],"genre_scores_gemma":[0.9953812,0.00003454179,0.003696514,0.00001488463,0.00001051082,0.0001156959,0.0006707506,0.000004835172,0.00007104209],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009422843,"threshold_uncertainty_score":0.04983336,"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."}}