{"id":"W2050476332","doi":"10.1016/j.jcct.2015.04.004","title":"Iterative reconstruction in cardiac CT","year":2015,"lang":"en","type":"review","venue":"Journal of cardiovascular computed tomography","topic":"Radiation Dose and Imaging","field":"Medicine","cited_by":51,"is_retracted":false,"has_abstract":false,"ca_institutions":"St. Paul's Hospital; University of British Columbia","funders":"","keywords":"Medicine; Iterative reconstruction; Image quality; Radiation dose; Radiology; Cardiac imaging; Image noise; Medical physics; Computed tomography; Noise (video); Nuclear medicine; Artificial intelligence; Image (mathematics); Computer science","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":[0.001946436,0.0003899492,0.004624942,0.002661078,0.00003273381,0.00007093218,0.0001897735,0.0001549665,0.000007170236],"category_scores_gemma":[0.00006874184,0.000305771,0.008387711,0.00191096,0.00006605616,0.0002903452,0.00003508517,0.001058656,0.000008985843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002538695,"about_ca_system_score_gemma":0.0007143505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006442696,"about_ca_topic_score_gemma":1.448468e-7,"domain_scores_codex":[0.9965078,0.0007530121,0.001312764,0.0002892363,0.0008884557,0.000248695],"domain_scores_gemma":[0.9978875,0.00007651639,0.0007548733,0.0004872307,0.0005365873,0.0002573043],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001179113,0.00003755478,0.0001963839,0.001798264,0.01107076,0.0006335133,0.0000438639,0.000120467,5.650715e-8,0.000004280112,0.001123973,0.9849591],"study_design_scores_gemma":[0.001288174,0.0001228525,0.0001097991,0.01438437,0.006958988,0.008016367,0.00002915399,0.00004040633,0.000002014987,0.00002361285,0.9687764,0.0002478652],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00008637582,0.9957904,0.0007900659,0.00001455263,0.001824187,0.0005386123,0.000009785318,0.00002065565,0.0009254019],"genre_scores_gemma":[0.0001125387,0.9967972,0.001521058,0.00002057349,0.001450415,0.00000837881,0.00003019805,0.00004921292,0.00001043262],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9847112,"threshold_uncertainty_score":0.9999394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03151821235973624,"score_gpt":0.2989018197643072,"score_spread":0.267383607404571,"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."}}