{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004163446,0.0006514979,0.0005787329,0.001773239,0.0002386641,0.001665692,0.0008182314,0.001672117,0.002029195],"category_scores_gemma":[0.003467326,0.0004188632,0.0002899286,0.0008313709,0.0008930086,0.0008648217,0.0007578315,0.00151848,0.001511918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003625284,"about_ca_system_score_gemma":0.0007183864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004527826,"about_ca_topic_score_gemma":0.0004625941,"domain_scores_codex":[0.9991558,0.0003949299,0.00006024073,0.0001129177,0.0002284718,0.00004762731],"domain_scores_gemma":[0.9986501,0.0006169346,0.0001430613,0.000108639,0.0003497712,0.0001314936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004610847,0.0001689156,0.005658212,0.001695456,0.00005990722,0.001499554,0.0002776281,0.002873334,0.1115897,0.005880757,0.01694269,0.8528928],"study_design_scores_gemma":[0.0003258101,0.002970364,0.06055976,0.004163893,0.0006948747,0.08819407,0.0007829909,0.1115329,0.1619763,0.04529245,0.5227365,0.0007700717],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03877518,0.1707801,0.7518324,0.01008435,0.001350721,0.0007500423,0.0006882896,0.003043255,0.02269552],"genre_scores_gemma":[0.212359,0.1275855,0.6423827,0.004313921,0.005235509,0.001050489,0.001049983,0.000713654,0.005309122],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004163446,"threshold_uncertainty_score":0.02201867,"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."}}