{"id":"W2147749486","doi":"10.1002/jmri.24413","title":"Improved MR venography using quantitative susceptibility-weighted imaging","year":2013,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance Imaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Telemedicine and Advanced Technology Research Center; Medical Research and Materiel Command; National Heart, Lung, and Blood Institute; National Institutes of Health; Wayne State University","keywords":"Susceptibility weighted imaging; Visualization; Artifact (error); Quantitative susceptibility mapping; Cerebral veins; Isotropy; Weighting; Medicine; Computer science; Nuclear medicine; Magnetic resonance imaging; Radiology; Nuclear magnetic resonance; Artificial intelligence; Physics; Optics","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":[],"consensus_categories":[],"category_scores_codex":[0.0003183753,0.0001908073,0.0003883679,0.0002727329,0.0001221122,0.000061141,0.0001754887,0.00003201473,0.0001972227],"category_scores_gemma":[0.0001138836,0.0001590091,0.0002147552,0.0004385505,0.0002189029,0.0004563671,0.00004911729,0.0004096515,0.000007212079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001032277,"about_ca_system_score_gemma":0.0001102193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007126238,"about_ca_topic_score_gemma":0.000001340638,"domain_scores_codex":[0.9983988,0.0000437958,0.0006916456,0.0002341241,0.0002874843,0.0003440864],"domain_scores_gemma":[0.9981948,0.00009907545,0.0004520147,0.0003255736,0.0007504768,0.0001780702],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001487111,0.0003080896,0.06576471,0.000061003,0.00001156903,0.00008989078,0.0003903797,0.00001924321,0.5115821,0.0004732436,0.001962206,0.4191889],"study_design_scores_gemma":[0.008916729,0.002247723,0.3371732,0.003121876,0.0007329429,0.005141327,0.005530308,0.4701121,0.04091194,0.03224184,0.09228759,0.001582484],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7199754,0.04204188,0.2298903,0.005440628,0.0002075401,0.001146337,0.000007969231,0.0001121899,0.001177782],"genre_scores_gemma":[0.5698684,0.0003721391,0.428995,0.0004759591,0.0001482043,0.00001669006,0.000001609245,0.00003563131,0.00008630203],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4706701,"threshold_uncertainty_score":0.6484201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01731297112596759,"score_gpt":0.3178518453652774,"score_spread":0.3005388742393098,"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."}}