{"id":"W2757859885","doi":"10.1016/j.cjca.2017.07.066","title":"MACHINE LEARNING OF THREE-DIMENSIONAL LEFT VENTRICULAR DEFORMATION FOR AUTOMATED DIAGNOSTIC SUPPORT IN AMYLOID, FABRY, AND HYPERTROPHIC CARDIOMYOPATHY: A CARDIOVASCULAR MRI IMAGING STUDY","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Cardiology","topic":"Cardiomyopathy and Myosin Studies","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Calgary Laboratory Services","funders":"","keywords":"Medicine; Hypertrophic cardiomyopathy; Left ventricular hypertrophy; Ejection fraction; Cardiology; Internal medicine; Cardiac amyloidosis; Cardiomyopathy; Fabry disease; Magnetic resonance imaging; Radial stress; Radiology; Heart failure; Deformation (meteorology)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.002714563,0.0006561689,0.000867654,0.001034607,0.000400907,0.001132331,0.0007478149,0.001077231,0.000705617],"category_scores_gemma":[0.007178523,0.000301662,0.0007772534,0.0005509552,0.0004837711,0.0006070805,0.0006720066,0.001023143,0.0002693918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000444865,"about_ca_system_score_gemma":0.0007422274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004708661,"about_ca_topic_score_gemma":0.004442764,"domain_scores_codex":[0.9994453,0.0002571792,0.00004700495,0.00009371849,0.00008519871,0.00007156301],"domain_scores_gemma":[0.9947608,0.0039703,0.0002845106,0.0003088835,0.0004569408,0.0002185901],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003885243,0.002563834,0.201299,0.000219509,0.0007066288,0.0008756378,0.0004611085,0.2447227,0.01642808,0.001214126,0.00384632,0.5237779],"study_design_scores_gemma":[0.00004417181,0.0002382971,0.03196944,0.00001408056,0.00004052645,0.0001324756,0.00008129649,0.9653563,0.00136923,0.0005826986,0.0001496247,0.00002184881],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.982832,0.0005675371,0.01520842,0.0004053246,0.00002643289,0.00004349174,0.0002345276,0.0001416893,0.0005404735],"genre_scores_gemma":[0.9933392,0.0001362065,0.005814751,0.000045471,0.0000255582,0.00001521482,0.0002989908,0.0000187715,0.0003057162],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004708661,"threshold_uncertainty_score":0.01435614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01384337052864229,"score_gpt":0.2450965533564733,"score_spread":0.2312531828278311,"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."}}