{"id":"W2887656847","doi":"10.1088/1361-6560/aad9be","title":"Coronary artery calcium quantification using contrast-enhanced dual-energy computed tomography scans in comparison with unenhanced single-energy scans","year":2018,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"CARE Canada","funders":"NIH Clinical Center; National Institutes of Health","keywords":"Medicine; Imaging phantom; Nuclear medicine; Radiology; Stenosis; Coronary artery calcium; Dual energy; Computed tomography; Bone mineral; Internal medicine; Osteoporosis","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":[],"consensus_categories":[],"category_scores_codex":[0.00009424937,0.0002319439,0.0004737942,0.0001945597,0.00006547902,0.0000079913,0.00008704601,0.00009071354,0.000004837305],"category_scores_gemma":[0.000006979431,0.000196542,0.0000207364,0.0006197814,0.0005225452,0.0001249662,0.00002143599,0.0001761383,4.981559e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005130242,"about_ca_system_score_gemma":0.00001688589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002625315,"about_ca_topic_score_gemma":0.0008772727,"domain_scores_codex":[0.9988078,0.00006471868,0.0003682654,0.000318146,0.00007449317,0.0003665865],"domain_scores_gemma":[0.9994925,0.0001139465,0.00008345885,0.0001758941,0.0000723433,0.00006182271],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001803298,0.0001817666,0.009123601,0.00004997402,0.00008146303,0.00001208507,0.001180205,0.02775019,0.9049104,0.005567795,0.00005705565,0.05090509],"study_design_scores_gemma":[0.004693943,0.001278773,0.01216392,0.0009177269,0.00007861221,0.0000457947,0.00199483,0.7954004,0.174189,0.007786403,0.0005724087,0.0008781119],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4347114,0.0006120402,0.5638651,0.00006343223,0.000240384,0.00006203925,0.000003870371,0.00006233525,0.000379452],"genre_scores_gemma":[0.9980022,0.00007159058,0.001219395,0.0002052081,0.0003740117,0.00001536223,0.00008370545,0.00002505203,0.000003416256],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7676502,"threshold_uncertainty_score":0.8014749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1064896710049394,"score_gpt":0.3299170493604383,"score_spread":0.2234273783554989,"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."}}