{"id":"W2811512141","doi":"10.1007/s10554-018-1407-0","title":"The ventricular residence time distribution derived from 4D flow particle tracing: a novel marker of myocardial dysfunction","year":2018,"lang":"en","type":"article","venue":"International journal of cardiac imaging","topic":"Cardiovascular Function and Risk Factors","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Health and Medical Research Council; Monash Institute of Medical Engineering, Monash University; Circle Cardiovascular Imaging; Baker Heart and Diabetes Institute","keywords":"Cardiology; Tracing; Residence time distribution; Internal medicine; Flow (mathematics); Distribution (mathematics); Particle (ecology); Residence time (fluid dynamics); Medicine; Mechanics; Physics; Computer science; Mathematics; Biology; Engineering; Mathematical analysis","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":[0.0007090188,0.0003970442,0.0005055447,0.001498157,0.0001873174,0.001361132,0.0004518492,0.001089718,0.0004952028],"category_scores_gemma":[0.001502007,0.0002892805,0.0002323089,0.0006834629,0.0002748325,0.00081487,0.0003081547,0.0005417247,0.000227256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002520024,"about_ca_system_score_gemma":0.0002725504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00092506,"about_ca_topic_score_gemma":0.0008637189,"domain_scores_codex":[0.9998354,0.00004009891,0.00001020759,0.00003624086,0.00005890578,0.00001917376],"domain_scores_gemma":[0.9994076,0.0002362156,0.0001845064,0.00005142429,0.00007222419,0.0000480173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.002498117,0.0002813248,0.1387933,0.0004794819,0.000276345,0.001774595,0.0005758229,0.01668466,0.632584,0.00233709,0.001999877,0.2017154],"study_design_scores_gemma":[0.0001338861,0.001076125,0.2981193,0.0001483206,0.0006190244,0.007865612,0.0003518701,0.4381379,0.2416088,0.003755305,0.007929045,0.0002547994],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7649906,0.004932696,0.2254452,0.000288512,0.0001129723,0.00008506166,0.0008332368,0.000978903,0.002332712],"genre_scores_gemma":[0.9551716,0.001314181,0.04179878,0.0001336675,0.000137436,0.00005466096,0.0003268274,0.0001327033,0.0009301117],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001498157,"threshold_uncertainty_score":0.003749669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007555888188777674,"score_gpt":0.24406121821473,"score_spread":0.2365053300259523,"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."}}