{"id":"W2146853585","doi":"10.1148/rg.284075031","title":"Steady-State MR Imaging Sequences: Physics, Classification, and Clinical Applications","year":2008,"lang":"en","type":"review","venue":"Radiographics","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":288,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"","keywords":"Steady state (chemistry); Medicine; Nuclear magnetic resonance; SIGNAL (programming language); Magnetic resonance imaging; Relaxation (psychology); Signal-to-noise ratio (imaging); Physics; Nuclear medicine; Radiology; Optics; Computer science; Internal medicine","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002041089,0.0004337838,0.001410352,0.0002850359,0.0002709667,0.00003313594,0.0002479322,0.0002668144,0.000003950571],"category_scores_gemma":[0.00002888794,0.0003731975,0.0007437925,0.001326171,0.0008276683,0.00009281417,0.00006114714,0.0008942944,0.00001932195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007093423,"about_ca_system_score_gemma":0.0003212251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000900665,"about_ca_topic_score_gemma":0.000001912859,"domain_scores_codex":[0.9975408,0.00008150921,0.001033191,0.0007944343,0.0002409494,0.0003090859],"domain_scores_gemma":[0.9976019,0.0002836394,0.0005990022,0.00102746,0.0001991537,0.0002888691],"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":[0.000001618952,0.000137106,0.000565234,0.001352906,0.00008081551,0.000008127082,0.00001319588,2.271629e-7,3.774487e-7,0.0069854,0.001529149,0.9893258],"study_design_scores_gemma":[0.0001712492,0.000032742,0.00009630452,0.001764794,0.0007829959,0.0004054838,0.00001581176,0.0001242965,2.422676e-7,0.002325726,0.9939436,0.0003367477],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[8.913873e-7,0.8660951,0.1302297,0.0001677813,0.00003892378,0.002201156,0.0001202424,0.0003297839,0.0008164637],"genre_scores_gemma":[0.00001177788,0.9680912,0.02928141,0.0001684067,0.0004491488,0.001160058,0.0005483374,0.00009575787,0.0001939369],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9924145,"threshold_uncertainty_score":0.999872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1072599254033397,"score_gpt":0.4318786553833625,"score_spread":0.3246187299800228,"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."}}