{"id":"W2020281558","doi":"10.1016/j.mri.2010.08.009","title":"A general dual-bolus approach for quantitative DCE-MRI","year":2010,"lang":"en","type":"article","venue":"Magnetic Resonance Imaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":46,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Hospital for Sick Children","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Bolus (digestion); Nuclear medicine; Medicine; Dynamic contrast-enhanced MRI; Biomedical engineering; Magnetic resonance imaging; Radiology; Surgery","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001028734,0.001249202,0.0008164891,0.0008003882,0.0004181245,0.001281273,0.00119258,0.001607789,0.006933731],"category_scores_gemma":[0.0008808013,0.001059838,0.0006252874,0.0007177572,0.000463417,0.001224874,0.001206073,0.002048442,0.006571682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003296872,"about_ca_system_score_gemma":0.0006951785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003504569,"about_ca_topic_score_gemma":0.0007386137,"domain_scores_codex":[0.9997512,0.00005225808,0.0000173397,0.00008415744,0.00007378756,0.00002138844],"domain_scores_gemma":[0.9998094,0.00005006639,0.00001289311,0.00006103428,0.00004947508,0.00001713826],"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":[0.000422636,0.0001038939,0.0004019842,0.0007190659,0.00007568664,0.0003389957,0.00008264444,0.002044937,0.873794,0.01346453,0.003624354,0.1049274],"study_design_scores_gemma":[0.000178549,0.0006654396,0.002314115,0.0001913423,0.000291423,0.009258454,0.00007652741,0.09264617,0.7372075,0.01270526,0.144258,0.0002072385],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00435772,0.001089431,0.9900063,0.0001711532,0.0001134944,0.0002588124,0.0002237401,0.0009031222,0.002876196],"genre_scores_gemma":[0.03860597,0.002381406,0.9480016,0.0003314753,0.0000803635,0.0006068473,0.0004560605,0.0007158112,0.008820491],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006933731,"threshold_uncertainty_score":0.02319562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01834757380044169,"score_gpt":0.331318409991136,"score_spread":0.3129708361906943,"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."}}