{"id":"W2935764869","doi":"10.1002/jmri.26747","title":"Quality‐based pharmacokinetic model selection on DCE‐MRI for characterizing orbital lesions","year":2019,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance Imaging","topic":"Ocular Oncology and Treatments","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"McMaster University","keywords":"Medicine; Pharmacokinetics; Area under the curve; Logistic regression; Receiver operating characteristic; Wilcoxon signed-rank test; Dynamic contrast-enhanced MRI; Population; Nuclear medicine; Radiology; Magnetic resonance imaging; Internal medicine; Mann–Whitney U test","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005508608,0.001028221,0.00102333,0.001540349,0.0003225247,0.001408328,0.0006683271,0.0006567326,0.0007895747],"category_scores_gemma":[0.01416607,0.0003969372,0.001424952,0.0006795918,0.0004219496,0.0007227875,0.0007255642,0.0006306361,0.0002543945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001110152,"about_ca_system_score_gemma":0.001151968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007387977,"about_ca_topic_score_gemma":0.004907983,"domain_scores_codex":[0.9986857,0.0006516764,0.00009228477,0.0002655353,0.000210493,0.00009435924],"domain_scores_gemma":[0.9934366,0.004441716,0.0008278128,0.000311484,0.0008318116,0.0001505431],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002304029,0.0004741665,0.245501,0.0003590399,0.001314398,0.0003637673,0.0001680166,0.6043907,0.0110727,0.0007933212,0.002731073,0.1305279],"study_design_scores_gemma":[0.00005536101,0.000226801,0.02272234,0.00002429709,0.0001769173,0.0001807541,0.00003082759,0.9724041,0.002880014,0.0007560299,0.0005153547,0.0000271809],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6959242,0.002172208,0.2964893,0.0008583265,0.00004871449,0.0004168546,0.001587442,0.001184634,0.001318288],"genre_scores_gemma":[0.9663063,0.0002630844,0.03081177,0.0001173061,0.00002604963,0.0001411257,0.001909842,0.00009347708,0.000331061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007387977,"threshold_uncertainty_score":0.0291326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02746934299446898,"score_gpt":0.3469724950544212,"score_spread":0.3195031520599522,"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."}}