{"id":"W2625188121","doi":"10.1161/circimaging.116.003951","title":"Recent Advances in Cardiovascular Magnetic Resonance","year":2017,"lang":"en","type":"review","venue":"Circulation Cardiovascular Imaging","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":186,"is_retracted":false,"has_abstract":true,"ca_institutions":"NOSM University","funders":"National Institute of Biomedical Imaging and Bioengineering; National Heart, Lung, and Blood Institute","keywords":"Medicine; Magnetic resonance imaging; Coronary artery disease; Cardiology; Heart failure; Myocardial fibrosis; Cardiac magnetic resonance imaging; Internal medicine; Radiology; Heart disease","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":[0.002121326,0.0008595672,0.005560749,0.0007947656,0.0002525949,0.0002256892,0.0003868275,0.0003189835,0.00003344403],"category_scores_gemma":[0.001609138,0.0008642725,0.008365136,0.0006752256,0.0002247374,0.0004403117,0.0001779641,0.0009661415,0.0001888456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007939662,"about_ca_system_score_gemma":0.0007011027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003911293,"about_ca_topic_score_gemma":0.000002111049,"domain_scores_codex":[0.9946141,0.0005355149,0.001131352,0.001493891,0.001429843,0.000795358],"domain_scores_gemma":[0.9948179,0.0002016592,0.0002806419,0.004064793,0.0003701157,0.0002649314],"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":[0.000004603529,0.00003582313,0.002519395,0.006265359,0.0008569193,0.001035935,0.00002664697,0.0002996732,2.362618e-8,0.00001178838,0.0001304354,0.9888134],"study_design_scores_gemma":[0.001321234,0.00000870756,0.005005949,0.01820325,0.009311951,0.001635308,0.0000149396,0.0001190179,2.062588e-7,0.00002861333,0.9635618,0.0007890525],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000001204345,0.9866142,0.0007468802,0.0000372448,0.001363458,0.001973275,0.00003536724,0.0001828466,0.009045489],"genre_scores_gemma":[0.00009769409,0.9975841,0.0002808825,0.00006005029,0.001079898,0.0003204832,0.0002826031,0.0002182061,0.00007610071],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9880244,"threshold_uncertainty_score":0.9993808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04461964683870135,"score_gpt":0.3298435456042464,"score_spread":0.2852238987655451,"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."}}