{"id":"W1964792401","doi":"10.1118/1.1582812","title":"A dynamic approach to identifying desired physiological phases for cardiac imaging using multislice spiral CT","year":2003,"lang":"en","type":"article","venue":"Medical Physics","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":121,"is_retracted":false,"has_abstract":true,"ca_institutions":"Continental (Canada)","funders":"","keywords":"Cardiac cycle; Multislice; Cardiac imaging; Coronary arteries; Image quality; Right coronary artery; Spiral (railway); Artery; Circumflex; Medicine; Biomedical engineering; Nuclear medicine; Artificial intelligence; Radiology; Computer science; Cardiology; Mathematics; Coronary angiography; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006958151,0.00101227,0.000473343,0.001392139,0.0002947868,0.00124662,0.0009358324,0.0009514173,0.001439188],"category_scores_gemma":[0.002112994,0.0006650204,0.0006990127,0.000768684,0.0005851536,0.001130566,0.0008284951,0.0006828132,0.0008327319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005849152,"about_ca_system_score_gemma":0.0009074503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001037211,"about_ca_topic_score_gemma":0.001371505,"domain_scores_codex":[0.9996122,0.00008258837,0.00002707133,0.0001017809,0.0001437887,0.00003258175],"domain_scores_gemma":[0.9995603,0.0001558686,0.0000701209,0.00009820281,0.00008616114,0.00002941362],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002639271,0.0001719339,0.007254467,0.0005235769,0.00009332018,0.0004488503,0.0002228833,0.1275013,0.3818304,0.02830145,0.001495574,0.4518923],"study_design_scores_gemma":[0.00005312002,0.0009125787,0.005586448,0.0001694564,0.0001333879,0.003707526,0.0002056642,0.8044901,0.1386337,0.01755049,0.02838199,0.0001754351],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01028477,0.0005827755,0.9876459,0.0001445946,0.00003877204,0.0000865318,0.00005193411,0.0003257738,0.000838983],"genre_scores_gemma":[0.1141386,0.001413227,0.8826513,0.00013382,0.00007000427,0.000227923,0.0001755713,0.0001493366,0.001040264],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001439188,"threshold_uncertainty_score":0.004814565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06792774164430257,"score_gpt":0.3547590297453196,"score_spread":0.286831288101017,"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."}}