{"id":"W2277068097","doi":"10.1117/1.jmi.3.1.016001","title":"Fabrication and control of CT number through polymeric composites based on coronary plaque CT phantom applications","year":2016,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; University Health Network","keywords":"Imaging phantom; Fabrication; Hounsfield scale; Biomedical engineering; Medicine; Thermoplastic polyurethane; Polyvinylidene fluoride; Materials science; Composite material; Polymer; Computed tomography; Nuclear medicine; Radiology; Elastomer","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002281977,0.0001126003,0.0002353303,0.00006442236,0.00004403537,0.00001202146,0.0001635176,0.00001542044,0.0001120554],"category_scores_gemma":[0.00007644622,0.0000791741,0.00005938339,0.0001077555,0.0001266934,0.0002924183,0.00001286009,0.000202097,0.00000618304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004415323,"about_ca_system_score_gemma":0.00003838264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004142008,"about_ca_topic_score_gemma":1.923699e-7,"domain_scores_codex":[0.9988864,0.00003529112,0.0003913128,0.00008976476,0.0004390253,0.0001582065],"domain_scores_gemma":[0.9990735,0.0004519895,0.0001665782,0.0001116849,0.00006731466,0.0001289521],"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":[0.00008853169,0.000196717,0.04992527,0.0001268434,0.0001197332,0.0001620657,0.0001274987,0.001760346,0.08012197,0.0005716048,0.000968739,0.8658307],"study_design_scores_gemma":[0.01740867,0.000148207,0.02928218,0.003846164,0.0004091635,0.004611628,0.0005465192,0.7629915,0.1194386,0.00411065,0.05615926,0.001047379],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02031952,0.001474975,0.973899,0.003455353,0.0001294651,0.00008348862,0.000007640738,0.00003625458,0.0005943112],"genre_scores_gemma":[0.9960225,0.0003699263,0.002915095,0.0004869788,0.0001728131,0.000006528967,0.000001243973,0.00001887054,0.000006072545],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9757029,"threshold_uncertainty_score":0.3228626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005083402073368344,"score_gpt":0.2559191669791666,"score_spread":0.2508357649057983,"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."}}