{"id":"W2135456932","doi":"10.1002/mrm.22819","title":"Extended graphical model for analysis of dynamic contrast‐enhanced MRI","year":2011,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"MRI in cancer diagnosis","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Robarts Clinical Trials","funders":"","keywords":"Partial volume; Positron emission tomography; Dynamic contrast-enhanced MRI; Graphical model; Nuclear medicine; Mathematics; Magnetic resonance imaging; Statistics; Radiology; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006118127,0.0002584706,0.001296812,0.000859219,0.00002237061,0.000002149559,0.0002465107,0.0001721181,0.0009450258],"category_scores_gemma":[0.0003367756,0.0002114138,0.0002309485,0.001745452,0.0005391006,0.00004286881,0.00003173161,0.0002383588,0.000002111356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009947329,"about_ca_system_score_gemma":0.0001035621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002680551,"about_ca_topic_score_gemma":0.0006673982,"domain_scores_codex":[0.9974787,0.00004797353,0.0009170338,0.0005745406,0.0005188286,0.0004629426],"domain_scores_gemma":[0.9983342,0.0003365368,0.0001932307,0.0007026854,0.0002534417,0.0001799342],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0139383,0.004624008,0.1967725,0.002153183,0.001913418,0.0002800422,0.02710533,0.001145116,0.05478953,0.009308942,0.01581773,0.6721519],"study_design_scores_gemma":[0.006283442,0.001844957,0.5204315,0.0006013918,0.002175972,0.000005122395,0.0002286362,0.4632435,0.001131466,0.003109583,0.0007219721,0.0002224186],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8614115,0.03069639,0.08969641,0.007269317,0.0004249348,0.003291611,0.0001165584,0.0001186544,0.006974571],"genre_scores_gemma":[0.9774859,0.005172344,0.01533169,0.0007530248,0.00005136769,0.0004554598,0.00003466857,0.00003466324,0.0006808632],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6719295,"threshold_uncertainty_score":0.9999682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03616885381330788,"score_gpt":0.3252763221668303,"score_spread":0.2891074683535224,"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."}}