{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009061474,0.0005148101,0.000337466,0.0007645123,0.0001206648,0.0005991928,0.0003682174,0.000779336,0.00343343],"category_scores_gemma":[0.004904735,0.0002849058,0.0002624815,0.000450293,0.0002535073,0.0005673222,0.0003159401,0.0008769981,0.0006539592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001099634,"about_ca_system_score_gemma":0.0002727804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005760276,"about_ca_topic_score_gemma":0.001138705,"domain_scores_codex":[0.9995748,0.000173636,0.00005729519,0.00008445018,0.00008471751,0.00002500055],"domain_scores_gemma":[0.9980682,0.0009503748,0.0002677329,0.0002572131,0.0003645538,0.00009203993],"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":[0.001654123,0.0002054351,0.01701169,0.0007180913,0.0001039903,0.001784543,0.0001888216,0.002698103,0.8113872,0.0004291026,0.002180863,0.1616381],"study_design_scores_gemma":[0.0008791199,0.006542723,0.2836717,0.0004481205,0.001436059,0.08539813,0.0003407704,0.07601925,0.5190495,0.001645815,0.02412027,0.0004485246],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.441971,0.007653622,0.5378109,0.001240736,0.0004724372,0.0005190787,0.0004476859,0.002791338,0.007093236],"genre_scores_gemma":[0.6284366,0.002061618,0.3662597,0.0005404094,0.0001952762,0.0001368287,0.0002283031,0.0004226719,0.001718574],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00343343,"threshold_uncertainty_score":0.01148599,"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."}}