{"id":"W3180863822","doi":"10.1007/s12350-021-02724-5","title":"Artificial intelligence-based attenuation correction; closer to clinical reality?","year":2021,"lang":"en","type":"letter","venue":"Journal of Nuclear Cardiology","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Libin Cardiovascular Institute of Alberta; University of Calgary","funders":"","keywords":"Medicine; Attenuation; Medical physics; Optics","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":[0.00789833,0.0005111704,0.001050676,0.0006764577,0.001281697,0.00425112,0.001706816,0.02586355,0.005209959],"category_scores_gemma":[0.05538505,0.0004114953,0.0006995588,0.0004347024,0.004617845,0.006039791,0.001772634,0.03011724,0.003471766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002293503,"about_ca_system_score_gemma":0.00222043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002242818,"about_ca_topic_score_gemma":0.003339639,"domain_scores_codex":[0.9948077,0.002893087,0.0005213661,0.0003315377,0.001263109,0.000183317],"domain_scores_gemma":[0.9617348,0.03041891,0.001156704,0.001116313,0.004085652,0.001487575],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002535923,0.0001249448,0.001863756,0.0003418637,0.0000910161,0.004843612,0.0005384342,0.001011194,0.0007962931,0.06285063,0.7679353,0.1593492],"study_design_scores_gemma":[0.0001637932,0.0001124638,0.0009325515,0.0006677161,0.00004431854,0.00565511,0.00091388,0.004427034,0.0005127901,0.1880097,0.7984851,0.00007555406],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0002287923,0.001897257,0.001440757,0.9909236,0.003441701,0.000004536698,0.0000120902,0.00002028806,0.002030871],"genre_scores_gemma":[0.0273428,0.007972177,0.01057657,0.8820295,0.06496759,0.00005541719,0.00004377628,0.00008016797,0.00693201],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.02586355,"threshold_uncertainty_score":0.04177088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.149189817521491,"score_gpt":0.4093528019909567,"score_spread":0.2601629844694657,"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."}}