{"id":"W2034928043","doi":"10.1002/1522-2586(200009)12:3<476::aid-jmri14>3.0.co;2-f","title":"3D MR DSA: Effects of injection protocol and image masking","year":2000,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance Imaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Heart, Lung, and Blood Institute","keywords":"Contrast (vision); Subtraction; Image quality; Digital subtraction angiography; Masking (illustration); Volume (thermodynamics); Image subtraction; Nuclear medicine; Magnetic resonance imaging; Medicine; Contrast medium; Radiology; Angiography; Image (mathematics); Computer science; Mathematics; Image processing; Artificial intelligence; Physics","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":[0.0001664161,0.00009382336,0.0002335393,0.00009182125,0.00004470258,0.00001389162,0.00005857848,0.00002559597,0.0001002627],"category_scores_gemma":[0.00006103334,0.00007688838,0.00005647386,0.0001489131,0.00009396372,0.0001603569,0.0000156457,0.0002048336,0.000001495517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000318497,"about_ca_system_score_gemma":0.00003733659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007289404,"about_ca_topic_score_gemma":2.5514e-7,"domain_scores_codex":[0.9991702,0.0000207971,0.0003790166,0.0001093086,0.0001869318,0.0001336821],"domain_scores_gemma":[0.9993882,0.0000577089,0.0002191836,0.0001364168,0.0001325575,0.0000658896],"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":[0.000170138,0.00009027222,0.00317968,0.0001801792,0.000001806149,0.00006040919,0.00009579068,0.00001188412,0.03999396,0.00003401814,0.0005661272,0.9556158],"study_design_scores_gemma":[0.01249744,0.004488884,0.2792519,0.006781148,0.0003267124,0.008514479,0.0002107154,0.01000488,0.1366846,0.005671258,0.5349814,0.0005865287],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5740911,0.02914586,0.2444266,0.008303292,0.0002666777,0.1093437,0.00001020498,0.0002943081,0.03411826],"genre_scores_gemma":[0.3632416,0.001453438,0.6246487,0.0007115015,0.0006488879,0.006981457,0.000001133588,0.00008685311,0.002226328],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9550292,"threshold_uncertainty_score":0.3135417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004976404010830571,"score_gpt":0.2982392746793867,"score_spread":0.2932628706685561,"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."}}