{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001313176,0.0006467002,0.0008686988,0.00394208,0.0005169671,0.0008402846,0.0004707573,0.0008314546,0.0004568843],"category_scores_gemma":[0.001642561,0.0001709645,0.0006735123,0.001080406,0.000268239,0.0002497147,0.0006087759,0.0003845198,0.0003408925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001767022,"about_ca_system_score_gemma":0.0006312428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004385104,"about_ca_topic_score_gemma":0.006294025,"domain_scores_codex":[0.9993868,0.0001135657,0.00007260637,0.0002202408,0.0001034772,0.0001032504],"domain_scores_gemma":[0.9991919,0.0003112365,0.00008598683,0.0001130443,0.0001932633,0.0001044142],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.004970349,0.002040692,0.3970775,0.0004344849,0.0009306135,0.003630537,0.0005569179,0.009777516,0.1798337,0.000698765,0.006814169,0.3932347],"study_design_scores_gemma":[0.0001850361,0.0007258389,0.6631274,0.00009011017,0.001146348,0.00486726,0.0008484378,0.2858399,0.03659313,0.00141726,0.005074496,0.0000845857],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9789304,0.0006595876,0.01743504,0.0001438384,0.00004531552,0.0000715737,0.001868982,0.0004294704,0.0004158698],"genre_scores_gemma":[0.9763277,0.00023892,0.01522106,0.00005631184,0.00009148467,0.00004050523,0.007433964,0.00004012071,0.0005499276],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004385104,"threshold_uncertainty_score":0.008719206,"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."}}