{"id":"W2964035784","doi":"10.1016/j.jmir.2019.06.023","title":"Establishing Guidelines for Quality Assurance and Clinical Application of Metal Artifact Reduction (O-MAR) Software in Radiation Therapy","year":2019,"lang":"en","type":"article","venue":"Journal of medical imaging and radiation sciences","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Southlake Regional Health Center","funders":"","keywords":"Quality assurance; Artifact (error); Vendor; Medical physics; Software; Image quality; Reduction (mathematics); Radiation therapy; Medicine; Computer science; Radiology; Artificial intelligence; Image (mathematics)","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":[],"consensus_categories":[],"category_scores_codex":[0.004570206,0.00006929175,0.0002219093,0.0001471032,0.000051162,0.00004679941,0.0001117818,0.00004713341,0.000003875179],"category_scores_gemma":[0.002130161,0.00005379081,0.00004599377,0.000198614,0.0001265785,0.0008947102,0.000008101569,0.0001599893,2.123301e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001922588,"about_ca_system_score_gemma":0.00006924145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001596146,"about_ca_topic_score_gemma":0.000002914679,"domain_scores_codex":[0.9985026,0.00006837323,0.0007555162,0.0001254882,0.0004390805,0.000108948],"domain_scores_gemma":[0.9987154,0.0006769343,0.0003266259,0.00005302874,0.0001552457,0.00007275262],"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":[0.00001584561,0.00001732438,0.1132672,0.00003659985,0.0000101125,2.220568e-7,0.0002119236,0.005835477,0.001398501,0.00006759263,0.0001284356,0.8790107],"study_design_scores_gemma":[0.002577089,0.0001201669,0.2524942,0.0002340066,0.0000235247,0.00006586777,0.002374731,0.730758,0.001722323,0.00254162,0.00686068,0.0002278402],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8075975,0.008223378,0.1806046,0.00274572,0.0006810178,0.0001171944,0.000001816894,0.00001474484,0.00001398574],"genre_scores_gemma":[0.9833683,0.00661929,0.009615212,0.0000927782,0.0002926902,0.000002549426,0.000001523509,0.000005134118,0.000002470824],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8787829,"threshold_uncertainty_score":0.2550155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04629892925031394,"score_gpt":0.4083023354042304,"score_spread":0.3620034061539165,"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."}}