{"id":"W4386590356","doi":"10.1093/ehjimp/qyad026","title":"Pre-screening for non-diagnostic coronary computed tomography angiography","year":2023,"lang":"en","type":"article","venue":"European Heart Journal - Imaging Methods and Practice","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Medicine; Logistic regression; Coronary artery disease; Confidence interval; Computed tomography angiography; Radiology; Computed tomography; Diagnostic accuracy; Coronary angiography; Angiography; Pre- and post-test probability; Area under the curve; Cohort; Internal medicine; Cardiology","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":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.008599308,0.0002656134,0.0004376385,0.0006463873,0.0005696365,0.0003327937,0.000105087,0.00003318143,0.00002114975],"category_scores_gemma":[0.01213497,0.00024665,0.0006172669,0.0008869731,0.0001377195,0.0004663634,0.0001250063,0.0006655715,0.00003177414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001426081,"about_ca_system_score_gemma":0.00005266686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001741994,"about_ca_topic_score_gemma":9.258846e-8,"domain_scores_codex":[0.9964937,0.001807281,0.0004796897,0.0003941912,0.0002926824,0.0005324635],"domain_scores_gemma":[0.9776828,0.02091729,0.0002325231,0.0002984658,0.0004071638,0.0004617255],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009651074,0.0002033712,0.552263,0.0002263617,0.00077866,0.002087828,0.001179393,0.0001948723,0.002637763,0.00002945191,0.2032814,0.2361528],"study_design_scores_gemma":[0.001547417,0.0002279095,0.6918243,0.0003777203,0.0008255002,0.01227109,0.0004236007,0.002158784,0.00006636071,0.0001320473,0.289894,0.0002513689],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0115506,0.01876804,0.9307446,0.02273533,0.002334585,0.001073217,0.00005191085,0.0004547472,0.01228699],"genre_scores_gemma":[0.0835637,0.0007546364,0.9075933,0.00637485,0.001333816,0.00001201117,0.00005595509,0.0001311497,0.0001805797],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2359015,"threshold_uncertainty_score":0.9999986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06480405042256315,"score_gpt":0.4227051319410563,"score_spread":0.3579010815184931,"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."}}