{"id":"W2986192434","doi":"10.1161/circimaging.119.009055","title":"Application of Hybrid Matrix Metalloproteinase-Targeted and Dynamic <sup>201</sup> Tl Single-Photon Emission Computed Tomography/Computed Tomography Imaging for Evaluation of Early Post-Myocardial Infarction Remodeling","year":2019,"lang":"en","type":"article","venue":"Circulation Cardiovascular Imaging","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"National Center for Research Resources; National Heart, Lung, and Blood Institute; U.S. Department of Veterans Affairs","keywords":"Single-photon emission computed tomography; Medicine; Myocardial infarction; Emission computed tomography; Saline; Ischemia; Blood flow; Perfusion; Nuclear medicine; Internal medicine; Matrix metalloproteinase; Positron emission tomography; Cardiology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006235788,0.000426165,0.0002441874,0.0004644107,0.0001166642,0.0004052758,0.0003381693,0.0006398515,0.001001255],"category_scores_gemma":[0.0002608019,0.0003397023,0.0002121109,0.0001863428,0.0003485954,0.0003350296,0.0002522916,0.000433487,0.0002266731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002792764,"about_ca_system_score_gemma":0.0002093032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003438313,"about_ca_topic_score_gemma":0.0005247807,"domain_scores_codex":[0.9998085,0.00004937699,0.00001020002,0.00004624568,0.0000530823,0.00003262499],"domain_scores_gemma":[0.9998112,0.00005408673,0.0000502902,0.00002003944,0.00003586714,0.00002849048],"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":[0.0003881353,0.00003514203,0.001349952,0.00004417187,0.00001079019,0.00009659569,0.000009122581,0.0001165791,0.9944931,0.00003864736,0.00002238244,0.003395308],"study_design_scores_gemma":[0.000124037,0.002193501,0.02379041,0.00002837956,0.0001671638,0.004676621,0.00006383429,0.009921939,0.9573018,0.0001202197,0.001578892,0.00003309886],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9455808,0.005356821,0.04660771,0.0001622584,0.00002098834,0.0001130527,0.000144303,0.0001876365,0.001826436],"genre_scores_gemma":[0.9425526,0.001599093,0.05438185,0.0001429567,0.00001873205,0.0001550062,0.0002018758,0.00002667063,0.0009211578],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001001255,"threshold_uncertainty_score":0.003349602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0089772735315617,"score_gpt":0.2514269445298781,"score_spread":0.2424496709983164,"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."}}