{"id":"W2065983783","doi":"10.1002/mrm.21761","title":"A human ferritin iron oxide nano‐composite magnetic resonance contrast agent","year":2008,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Iron Metabolism and Disorders","field":"Medicine","cited_by":143,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre","funders":"National Institute of Biomedical Imaging and Bioengineering; National Heart, Lung, and Blood Institute; National Institutes of Health; Kowa Company; Covis Pharma","keywords":"Maghemite; Ferritin; Iron oxide; In vivo; Iron oxide nanoparticles; Magnetite; Magnetic resonance imaging; Macrophage; Chemistry; Contrast (vision); In vitro; MRI contrast agent; Nuclear magnetic resonance; Nanoparticle; Inflammation; Biophysics; Materials science; Biochemistry; Nanotechnology; Medicine; Immunology; Biology; Radiology","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":[0.0001827769,0.0002074647,0.0001661432,0.0001440871,0.0001218793,0.0001343762,0.0002135348,0.0003629537,0.0007039155],"category_scores_gemma":[0.0001367192,0.00009973135,0.0001120454,0.00005993872,0.0001077442,0.0001150487,0.0001015754,0.0002326254,0.0002771695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001883183,"about_ca_system_score_gemma":0.0001339278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005248454,"about_ca_topic_score_gemma":0.0006309989,"domain_scores_codex":[0.9999446,0.00001210209,0.000002262917,0.00001681058,0.00001571675,0.000008484867],"domain_scores_gemma":[0.9999523,0.00001288782,0.000007124393,0.000003552194,0.00001083411,0.00001327255],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009652595,0.00004646536,0.00004031298,0.00003471794,0.000003786311,0.00007131816,0.000007516649,0.00008575957,0.9956791,0.0001063183,0.0001414192,0.003686826],"study_design_scores_gemma":[0.00005097939,0.0005509976,0.0006484223,0.000006862202,0.0000201461,0.0004584235,0.000006217328,0.002063749,0.9891855,0.00003255591,0.006968037,0.000008180679],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9351292,0.006463461,0.04796782,0.0006448809,0.000274969,0.0002900327,0.0002423288,0.0005612249,0.008426007],"genre_scores_gemma":[0.9545594,0.001264658,0.03603924,0.0003080056,0.00005057659,0.0000957274,0.0002770752,0.00004167695,0.007363632],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007039155,"threshold_uncertainty_score":0.002354801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01766411532026761,"score_gpt":0.2683877759618213,"score_spread":0.2507236606415537,"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."}}