{"id":"W1528719072","doi":"10.1002/jmri.22008","title":"Early identification of aortic valve sclerosis using iron oxide enhanced MRI","year":2009,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance Imaging","topic":"Cardiac Valve Diseases and Treatments","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"National Heart, Lung, and Blood Institute","keywords":"In vivo; Aortic valve; Ferumoxytol; Ex vivo; Magnetic resonance imaging; Medicine; Infiltration (HVAC); Pathology; Iron oxide; Radiology; Chemistry; Materials science; Internal medicine; Biology","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":[0.0005269573,0.0003993587,0.0003243375,0.000292485,0.0001240582,0.0003094383,0.0001758988,0.0005453725,0.000648886],"category_scores_gemma":[0.0003125488,0.0001899227,0.0001690169,0.00007854905,0.0004219567,0.0002861147,0.0001578874,0.0004728869,0.0001860014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001620695,"about_ca_system_score_gemma":0.0001541979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003034614,"about_ca_topic_score_gemma":0.0005568882,"domain_scores_codex":[0.9998422,0.00004266452,0.00001378341,0.00003140456,0.00003915766,0.0000306747],"domain_scores_gemma":[0.9997602,0.00004616981,0.0000830818,0.00002478961,0.00003732098,0.00004847034],"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":[0.0004277302,0.0000648016,0.0005389091,0.00003908904,0.000003570787,0.00007421815,0.00001744205,0.00001427829,0.9978206,0.00002312795,0.00001059103,0.0009657047],"study_design_scores_gemma":[0.00005218633,0.003151646,0.0106966,0.00001668853,0.0000470406,0.0008699597,0.00005370372,0.0006622067,0.9835835,0.00003358043,0.0008259896,0.000006951206],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9927325,0.00200106,0.003893014,0.00008539332,0.00002643105,0.00006043956,0.00003712386,0.00005753011,0.001106571],"genre_scores_gemma":[0.9910039,0.001200889,0.005797417,0.00007058877,0.00002117096,0.0000464974,0.00009253482,0.00001196304,0.001755042],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.000648886,"threshold_uncertainty_score":0.002786875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01339645789405468,"score_gpt":0.3080128039430103,"score_spread":0.2946163460489556,"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."}}