{"id":"W1618346903","doi":"10.1016/j.echo.2015.07.027","title":"Feasibility of Automated Three-Dimensional Rotational Mechanics by Real-Time Volume Transthoracic Echocardiography: Preliminary Accuracy and Reproducibility Data Compared with Cardiovascular Magnetic Resonance","year":2015,"lang":"en","type":"article","venue":"Journal of the American Society of Echocardiography","topic":"Cardiovascular Function and Risk Factors","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto General Hospital; University of Toronto","funders":"","keywords":"Medicine; Reproducibility; Magnetic resonance imaging; Coronary artery disease; Cardiology; Ventricle; Concordance correlation coefficient; Speckle tracking echocardiography; Feature tracking; Steady-state free precession imaging; Nuclear medicine; Radiology; Internal medicine; Artificial intelligence; Ejection fraction; Heart failure; Pattern recognition (psychology); Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.005763802,0.0003638423,0.002007068,0.0002072541,0.00012679,0.00001986453,0.0005815352,0.0001173445,0.000006411915],"category_scores_gemma":[0.00036613,0.0002470145,0.003899691,0.002210726,0.001353095,0.0002813388,0.0002034311,0.0005217328,5.514163e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009165399,"about_ca_system_score_gemma":0.0004865135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004920318,"about_ca_topic_score_gemma":0.000002102609,"domain_scores_codex":[0.9949,0.000647376,0.001058085,0.0009083269,0.002178386,0.0003078638],"domain_scores_gemma":[0.9933072,0.0002120673,0.001095805,0.003625456,0.00142607,0.0003334222],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005346765,0.0005753951,0.9173368,0.0002336556,0.00957159,0.0000163683,0.000362244,0.00436216,0.002845197,0.000001145674,0.04717786,0.01217085],"study_design_scores_gemma":[0.003918698,0.001978997,0.9724147,0.0003016732,0.00416862,0.000488345,0.0006892231,0.01246927,0.0004288809,0.00009126469,0.002731483,0.0003188883],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9800933,0.01579819,0.002051517,0.0004404512,0.0002047983,0.0009328043,0.0003749945,0.00007955683,0.00002439335],"genre_scores_gemma":[0.9820427,0.002282187,0.01534031,0.0001035664,0.0001091946,0.000006585318,0.00005827157,0.00004675439,0.00001038702],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05507789,"threshold_uncertainty_score":0.9999982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03380758259338984,"score_gpt":0.2817545308921292,"score_spread":0.2479469482987394,"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."}}