{"id":"W2071026440","doi":"10.1118/1.3622611","title":"A method for patient dose reduction in dynamic contrast enhanced CT study","year":2011,"lang":"en","type":"article","venue":"Medical Physics","topic":"MRI in cancer diagnosis","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University Health Network; Princess Margaret Cancer Centre","funders":"Canadian Institutes of Health Research; Terry Fox Foundation","keywords":"Reduction (mathematics); Medical imaging; Contrast (vision); Computed tomography; Nuclear medicine; Medicine; Medical physics; Radiology; Computer science; Computer vision; Mathematics","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":[0.0011346,0.0006856995,0.000465182,0.001145937,0.0003317135,0.000635412,0.0008498341,0.0006779439,0.003171554],"category_scores_gemma":[0.00413239,0.0003569688,0.0007244926,0.0007499863,0.0003205454,0.0005313245,0.0008946207,0.0008354142,0.001209139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004545276,"about_ca_system_score_gemma":0.0008057676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000966647,"about_ca_topic_score_gemma":0.001119819,"domain_scores_codex":[0.9992467,0.0001710533,0.00004504751,0.0001667484,0.000337676,0.00003273132],"domain_scores_gemma":[0.9987663,0.0004568304,0.0001423191,0.0002802829,0.0003164192,0.00003792009],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002987266,0.0001014381,0.002867962,0.0002547648,0.0001094467,0.0001646659,0.0001216573,0.04041414,0.07108946,0.008738436,0.004151313,0.8716879],"study_design_scores_gemma":[0.0000842365,0.0002632595,0.007318831,0.00006566167,0.0001775935,0.001562074,0.00005806514,0.8586173,0.08780579,0.008711471,0.03524138,0.00009421993],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003297541,0.000153922,0.9952359,0.00006337419,0.00002799256,0.00005294657,0.00004944444,0.0006788694,0.0004400331],"genre_scores_gemma":[0.04923017,0.0001858581,0.9485979,0.00007553222,0.0000484197,0.0001725729,0.0001759207,0.0002548262,0.001258705],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003171554,"threshold_uncertainty_score":0.01060992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03415530784423859,"score_gpt":0.3601384744039701,"score_spread":0.3259831665597315,"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."}}