{"id":"W2787727062","doi":"10.1161/circresaha.117.312535","title":"Noninvasive Immunometabolic Cardiac Inflammation Imaging Using Hyperpolarized Magnetic Resonance","year":2018,"lang":"en","type":"article","venue":"Circulation Research","topic":"Cardiac Fibrosis and Remodeling","field":"Medicine","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Engineering and Physical Sciences Research Council; Novo Nordisk Fonden; British Heart Foundation; Novo Nordisk; National Institute for Health and Care Research","keywords":"Inflammation; Medicine; Magnetic resonance imaging; Hyperpolarization (physics); Macrophage polarization; Macrophage; Proinflammatory cytokine; Internal medicine; Chemistry; In vitro; Nuclear magnetic resonance spectroscopy; Biochemistry","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":[],"consensus_categories":[],"category_scores_codex":[0.001365284,0.0001203816,0.0002881303,0.0004567513,0.000449146,0.0001186731,0.00008219371,0.00008914808,0.000118803],"category_scores_gemma":[0.0003415006,0.0001153078,0.0001636463,0.0008681678,0.0002630099,0.0002224609,0.00008329452,0.0003222607,0.0001527408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000213454,"about_ca_system_score_gemma":0.0002895838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003159164,"about_ca_topic_score_gemma":0.000002540716,"domain_scores_codex":[0.9978665,0.0002330805,0.0003214366,0.0003480938,0.0007940476,0.0004368265],"domain_scores_gemma":[0.9980091,0.0001033497,0.00005266808,0.0004561093,0.001255537,0.0001232452],"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":[0.00009407973,0.00001836992,0.08382148,0.00004711811,0.00002145234,0.000005663833,0.0005835799,0.0001506368,0.865879,0.0003748431,0.00006041008,0.04894337],"study_design_scores_gemma":[0.001109705,0.00005939108,0.815975,0.0002367223,0.00006544442,0.00006295743,0.0002976468,0.166527,0.009233741,0.0002238216,0.006021749,0.000186804],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9850649,0.008798089,0.0008821517,0.0002975501,0.00028757,0.0006355022,0.000005532187,0.00005304864,0.003975701],"genre_scores_gemma":[0.9969131,0.0001538472,0.001349065,0.00003294858,0.001270201,0.00001651681,0.00002810785,0.00003608988,0.000200101],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8566453,"threshold_uncertainty_score":0.4702117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06506587964241937,"score_gpt":0.3642731230114101,"score_spread":0.2992072433689907,"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."}}