{"id":"W2951641907","doi":"10.1016/j.mri.2019.06.007","title":"Automatic identification of atherosclerosis subjects in a heterogeneous MR brain imaging data set","year":2019,"lang":"en","type":"article","venue":"Magnetic Resonance Imaging","topic":"Cerebrovascular and Carotid Artery Diseases","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"Foothills Medical Centre; University of Calgary","funders":"Canadian Institutes of Health Research; Canada Foundation for Innovation; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Hotchkiss Brain Institute; Hotchkiss Brain Institute, University of Calgary; Health Research","keywords":"Cohort; Magnetic resonance imaging; Support vector machine; Artificial intelligence; Medicine; Data set; Linear discriminant analysis; Neuroimaging; Stroke (engine); Pattern recognition (psychology); Biomarker; Computer science; Cardiology; Internal medicine; Radiology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004320047,0.0001746762,0.0003443786,0.0001731396,0.00002974482,0.00005253879,0.0003227628,0.00002267445,0.0004350042],"category_scores_gemma":[0.0001530836,0.0001759241,0.0001035133,0.0003048814,0.00008613423,0.0002318227,0.0001634381,0.0001075686,0.0000817268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005516487,"about_ca_system_score_gemma":0.00008207478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001580801,"about_ca_topic_score_gemma":0.00001913046,"domain_scores_codex":[0.9981611,0.00008980956,0.000519597,0.0005319047,0.0003790023,0.0003185234],"domain_scores_gemma":[0.9982947,0.00008178538,0.0001275221,0.001357793,0.00005910528,0.00007913363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001665416,0.0001139966,0.8249211,0.0002759142,0.000006735333,0.00001269098,0.0003298807,0.00001411735,0.03088884,0.000008729462,0.0003738941,0.1430375],"study_design_scores_gemma":[0.001845221,0.00004180741,0.8757353,0.0005841975,0.00006253574,0.0001090799,0.000307875,0.1164056,0.003343843,0.00004365162,0.001328121,0.0001927922],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920227,0.006149969,0.00005512077,0.0006173692,0.0001133253,0.0006432461,0.00005488181,0.00006308958,0.000280308],"genre_scores_gemma":[0.9987742,0.0000989805,0.0002168981,0.0004471678,0.00003366019,0.00002674095,0.0001085467,0.00003639783,0.0002574628],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1428447,"threshold_uncertainty_score":0.7173977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01402667873736105,"score_gpt":0.2627588041191543,"score_spread":0.2487321253817933,"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."}}