{"id":"W4406987319","doi":"10.1148/ryct.240342","title":"Modified CT Technique Improves Image Quality for Assessment of Cardiac Conduction Device Lead Perforation","year":2025,"lang":"en","type":"article","venue":"Radiology Cardiothoracic Imaging","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto General Hospital; University Health Network; University of Toronto","funders":"","keywords":"Lead (geology); Image quality; Perforation; Medicine; Quality (philosophy); Biomedical engineering; Radiology; Materials science; Image (mathematics); Artificial intelligence; Computer science; Composite material; Geology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001099961,0.0002512957,0.0006432666,0.0002283901,0.0001514088,0.00002883281,0.0001544512,0.00007386639,0.000001988283],"category_scores_gemma":[0.0001090664,0.0002764997,0.0002471888,0.0002613601,0.0001580399,0.0004586272,0.00003470898,0.000307951,8.196269e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002885043,"about_ca_system_score_gemma":0.0000919239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001957617,"about_ca_topic_score_gemma":8.082199e-7,"domain_scores_codex":[0.9984711,0.0001884637,0.0005186563,0.0003511346,0.0001062643,0.0003643517],"domain_scores_gemma":[0.9990466,0.0002270304,0.0001149463,0.0003931858,0.0001826002,0.00003568043],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002182165,0.00001218893,0.002274733,0.0006459855,0.0002031915,0.000001907617,0.00007237995,0.0152826,0.90934,0.00166134,0.000257665,0.07022616],"study_design_scores_gemma":[0.0008949619,0.00005293185,0.01080966,0.0002165367,0.0004245515,0.00007767784,0.001248831,0.2840387,0.6923634,0.003478939,0.005482719,0.0009111329],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08750761,0.002147461,0.9026595,0.0001361154,0.001075737,0.0008621572,0.00004116189,0.0003561671,0.005214035],"genre_scores_gemma":[0.9486112,0.0001127691,0.05051762,0.00005130527,0.0001427042,0.0003944198,0.00006541337,0.00003732101,0.00006726882],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8611036,"threshold_uncertainty_score":0.9999687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01961272180099369,"score_gpt":0.3549110544746831,"score_spread":0.3352983326736894,"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."}}