{"id":"W2028584210","doi":"10.1118/1.4889518","title":"WE‐G‐18A‐07: Clinical Evaluation of Normalized Metal Artifact Reduction in KVCT Using MVCT Prior Images (MVCT‐NMAR) Technique in Radiotherapy","year":2014,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Alberta; Sunnybrook Health Science Centre","funders":"","keywords":"Tomotherapy; Medicine; Artifact (error); Nuclear medicine; Radiation treatment planning; Scanner; Reduction (mathematics); Medical imaging; Radiation therapy; Radiology; Computer vision; Artificial intelligence; Computer science; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002620497,0.0001740366,0.0004400233,0.00009818024,0.00002577034,0.000008966234,0.0001460473,0.0001507284,0.0000874214],"category_scores_gemma":[0.0003596396,0.0001699669,0.000105333,0.0003334353,0.0001351399,0.0002923841,0.00002301611,0.0005270786,0.000005228776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001608429,"about_ca_system_score_gemma":0.00008780574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002706481,"about_ca_topic_score_gemma":0.000006258803,"domain_scores_codex":[0.9979069,0.0003021789,0.0006676736,0.0002254749,0.0006353216,0.0002624115],"domain_scores_gemma":[0.9993615,0.0001544055,0.0001062575,0.0002328854,0.00005812159,0.00008682562],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006912032,0.0002725507,0.002515573,0.0001298055,0.00004771175,0.00000452943,0.0004047038,0.04429793,0.07604677,0.00009219653,0.00003467836,0.8760844],"study_design_scores_gemma":[0.003414543,0.00009890837,0.003798856,0.0004956435,0.000080272,0.00001612892,0.00007681142,0.815655,0.1668736,0.008498018,0.0005701268,0.0004221003],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5778617,0.000412728,0.4204263,0.00009645688,0.0003519414,0.0004734881,0.000001871233,0.00009040086,0.0002850907],"genre_scores_gemma":[0.9919163,0.0002577454,0.007302376,0.00002489008,0.0003932661,0.0000565121,0.000008072997,0.00003675671,0.000004086005],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8756623,"threshold_uncertainty_score":0.6931049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03494583812716582,"score_gpt":0.3629907214774626,"score_spread":0.3280448833502968,"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."}}