{"id":"W2162842306","doi":"10.4137/mri.s23557","title":"Iron Oxide as an Mri Contrast Agent for Cell Tracking: Supplementary Issue","year":2015,"lang":"en","type":"review","venue":"Magnetic Resonance Insights","topic":"Nanoparticle-Based Drug Delivery","field":"Materials Science","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hotchkiss Brain Institute; University of Calgary","funders":"","keywords":"Contrast (vision); Magnetic resonance imaging; Ex vivo; Iron oxide; In vivo; Contrast enhancement; Multiple sclerosis; Cell; Stroke (engine); Medicine; Pathology; Biomedical engineering; Materials science; Chemistry; Radiology; Computer science; Biology; Immunology; Artificial intelligence","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":[0.0002846634,0.0006896184,0.0005471412,0.001885739,0.000263982,0.0006493243,0.0004855407,0.001008288,0.03172661],"category_scores_gemma":[0.0004020228,0.0002623311,0.0002960751,0.001145434,0.0002110333,0.001258206,0.0006724374,0.0008937718,0.01305762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003183939,"about_ca_system_score_gemma":0.0006140209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000544881,"about_ca_topic_score_gemma":0.001132296,"domain_scores_codex":[0.9998946,0.00001093158,0.0000106356,0.00001324256,0.00005506476,0.00001556859],"domain_scores_gemma":[0.9998616,0.00004324587,0.00002169718,0.000008501577,0.00004821775,0.00001660985],"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":[0.00006405933,0.0001094865,0.00007454649,0.01405925,0.00002372532,0.0003112801,0.00005603481,0.0001959154,0.02030925,0.00768972,0.1564741,0.8006327],"study_design_scores_gemma":[0.000006023087,0.00004168203,0.0002140156,0.0006319985,0.00001300325,0.0006289662,0.00001290798,0.00005799934,0.00277654,0.0007084695,0.9949007,0.000007766295],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001702698,0.9281636,0.003108481,0.001681067,0.008653332,0.0001481622,0.0006058544,0.0001561584,0.05578057],"genre_scores_gemma":[0.01067021,0.9104053,0.003541843,0.001613242,0.004515245,0.0001131091,0.001253237,0.00005626287,0.06783158],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.03172661,"threshold_uncertainty_score":0.1061361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03843920891054372,"score_gpt":0.3051730696630469,"score_spread":0.2667338607525032,"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."}}