{"id":"W3134128846","doi":"10.1177/2048004021999900","title":"Aortic and mitral flow quantification using dynamic valve tracking and machine learning: Prospective study assessing static and dynamic plane repeatability, variability and agreement","year":2021,"lang":"en","type":"article","venue":"JRSM Cardiovascular Disease","topic":"Cardiac Valve Diseases and Treatments","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Circle Cardiovascular Imaging; Libin Cardiovascular Institute of Alberta; Alberta Children's Hospital; University of Calgary","funders":"Alberta Innovates; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Medicine; Cardiology; Mitral valve; Reproducibility; Regurgitant fraction; Internal medicine; Aortic valve; Hemodynamics; Repeatability; Blood flow; Ejection fraction; Mitral regurgitation; Magnetic resonance imaging; Aorta; Prospective cohort study; Radiology; Heart failure; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004676268,0.0004468343,0.0004683334,0.0009739167,0.0003612123,0.0007921695,0.0004246105,0.000855147,0.000625524],"category_scores_gemma":[0.007074114,0.0005667725,0.0003560918,0.000487318,0.0004893067,0.0006985365,0.0006011534,0.000452775,0.0002845182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001359731,"about_ca_system_score_gemma":0.0002049518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004355143,"about_ca_topic_score_gemma":0.0003555184,"domain_scores_codex":[0.9982418,0.0005740574,0.0001740763,0.0006058936,0.000293919,0.0001102654],"domain_scores_gemma":[0.9939473,0.002204038,0.001570208,0.0009323832,0.0008697218,0.0004763562],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001410725,0.0003600287,0.9822646,0.00004226263,0.0002014188,0.0001241384,0.0003237665,0.0004455294,0.004072872,0.00006431957,0.00006688133,0.01062346],"study_design_scores_gemma":[0.00008376635,0.002750495,0.9888236,0.00001723642,0.0001572212,0.0009580167,0.0002009423,0.004639064,0.001899269,0.0001041911,0.0003374261,0.00002862881],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997622,0.0002122402,0.00190582,0.000006392391,0.000005890491,0.00001772952,0.00005414952,0.00001327315,0.0001624609],"genre_scores_gemma":[0.9985924,0.00005269201,0.001132994,0.000008857078,0.00001114394,0.00001512515,0.000090095,0.000006746513,0.00008992942],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004676268,"threshold_uncertainty_score":0.02473074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02188044550121134,"score_gpt":0.3256019595426848,"score_spread":0.3037215140414735,"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."}}