{"id":"W3170169528","doi":"10.1093/ehjci/jeab112","title":"Clinical intra-cardiac 4D flow CMR: acquisition, analysis, and clinical applications","year":2021,"lang":"en","type":"article","venue":"European Heart Journal - Cardiovascular Imaging","topic":"Cardiovascular Function and Risk Factors","field":"Medicine","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Circle Cardiovascular Imaging; European Association of Cardiovascular Imaging; Türk Kardiyoloji Derneği","keywords":"Medicine; Flow (mathematics); Pulsatile flow; Magnetic resonance imaging; Stroke volume; Modalities; Flow velocity; Cardiology; Radiology; Internal medicine; Heart failure; Mechanics; Ejection fraction","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","metaepi_broad"],"consensus_categories":[],"category_scores_codex":[0.009110861,0.0003364103,0.001696916,0.0003624549,0.0006197831,0.0004134825,0.0001339536,0.0001031298,0.0002187772],"category_scores_gemma":[0.0004842279,0.0003020599,0.01276858,0.001115443,0.000347501,0.0002236419,0.0001869872,0.001445337,0.0002199362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000822,"about_ca_system_score_gemma":0.0002758695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007680494,"about_ca_topic_score_gemma":0.000001109467,"domain_scores_codex":[0.9928112,0.003442024,0.001410407,0.0008937289,0.0009645891,0.0004780551],"domain_scores_gemma":[0.9964063,0.0002377013,0.0001671682,0.001420743,0.0008184548,0.0009496065],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003873743,0.0001638916,0.7575995,0.00002966047,0.03154312,0.001359581,0.00004223886,0.0002116078,0.00005650579,0.00001660241,0.004812554,0.204126],"study_design_scores_gemma":[0.001380275,0.00003156457,0.5560625,0.00004858416,0.01236973,0.004408801,0.0001398298,0.0003424691,0.00002604431,0.00001365673,0.4249444,0.0002321166],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1181775,0.1307835,0.7335083,0.003711117,0.004974765,0.001062337,0.00005293206,0.0004653985,0.007264223],"genre_scores_gemma":[0.860092,0.05595621,0.05566252,0.00639942,0.02032015,0.00002167436,0.0003837607,0.0003351029,0.0008291229],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7419146,"threshold_uncertainty_score":0.9999431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02681396678391166,"score_gpt":0.3285467623404754,"score_spread":0.3017327955565637,"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."}}