{"id":"W3154444464","doi":"10.24908/iqurcp.10104","title":"13. Dual Energy Computed Tomography for Perfusion Imaging","year":2018,"lang":"en","type":"article","venue":"Inquiry Queen s Undergraduate Research Conference Proceedings","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Perfusion scanning; Perfusion; Calibration; Medicine; Iodinated contrast; Biomedical engineering; Contrast (vision); Nuclear medicine; Radiology; Computed tomography; Computer science; Physics; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0009566799,0.0005171302,0.0003341027,0.00103352,0.000319519,0.001301722,0.0006343434,0.001292229,0.02336669],"category_scores_gemma":[0.001304105,0.0005340646,0.0003941014,0.0008495682,0.0003964138,0.000765988,0.0005978232,0.0008480286,0.008122792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007370148,"about_ca_system_score_gemma":0.0006822617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001226665,"about_ca_topic_score_gemma":0.002254953,"domain_scores_codex":[0.9995311,0.00008269177,0.00003661637,0.00007090149,0.0002395524,0.0000392283],"domain_scores_gemma":[0.999499,0.00012078,0.00003900738,0.00006656967,0.0002449894,0.00002976694],"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":[0.0005442657,0.00009603452,0.003868385,0.00142013,0.00008920162,0.001093046,0.0001736894,0.005558015,0.3567412,0.03708032,0.05856189,0.5347738],"study_design_scores_gemma":[0.0001524548,0.0002083025,0.01234988,0.0006206551,0.0001599398,0.00536673,0.000151849,0.09378012,0.3569165,0.0121222,0.5179724,0.0001989965],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.01560653,0.007719828,0.8865029,0.003483838,0.0008917055,0.0003923987,0.002486983,0.007372373,0.07554349],"genre_scores_gemma":[0.1313727,0.006469744,0.7996959,0.001548873,0.0002575383,0.0006411418,0.002149261,0.001007204,0.05685764],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.02336669,"threshold_uncertainty_score":0.07816935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04796784414589612,"score_gpt":0.3275864873132434,"score_spread":0.2796186431673473,"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."}}