{"id":"W4411037342","doi":"10.1161/circimaging.124.017761","title":"Machine Learning to Automatically Differentiate Hypertrophic Cardiomyopathy, Cardiac Light Chain, and Cardiac Transthyretin Amyloidosis: A Multicenter CMR Study","year":2025,"lang":"en","type":"article","venue":"Circulation Cardiovascular Imaging","topic":"Amyloidosis: Diagnosis, Treatment, Outcomes","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre; GDI Integrated Facility Services (Canada)","funders":"","keywords":"Medicine; Cardiac amyloidosis; Hypertrophic cardiomyopathy; Transthyretin; Restrictive cardiomyopathy; Cardiomyopathy; Amyloidosis; Internal medicine; Cardiology; AL amyloidosis; Stage (stratigraphy); Cardiac magnetic resonance imaging; Heart failure; Radiology; Magnetic resonance imaging; Immunoglobulin light chain","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":[0.009008076,0.0006899767,0.0004427156,0.0006525758,0.0003143154,0.0005838906,0.0005090736,0.0005666375,0.0004423637],"category_scores_gemma":[0.009625908,0.0002419086,0.0005806795,0.0002527862,0.0004337899,0.0005552499,0.0005540692,0.0004478144,0.000188767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003692866,"about_ca_system_score_gemma":0.000418981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001267768,"about_ca_topic_score_gemma":0.001196139,"domain_scores_codex":[0.9980041,0.0012378,0.0001282733,0.000404278,0.0001355142,0.00008997008],"domain_scores_gemma":[0.9946395,0.002701399,0.0006388857,0.0006509401,0.0009114646,0.0004578736],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.007254739,0.003180463,0.9074896,0.00007752411,0.0006272184,0.0003402087,0.0005839358,0.005883572,0.009548404,0.000132397,0.0006659773,0.06421594],"study_design_scores_gemma":[0.001276776,0.01375813,0.7682455,0.00004181477,0.0006389008,0.001809733,0.0006633005,0.2062489,0.006038735,0.0003637848,0.0008413848,0.00007289615],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984781,0.00008712758,0.001183077,0.00004035989,0.000004662014,0.00005147209,0.0000446003,0.00002043842,0.00009022269],"genre_scores_gemma":[0.9966418,0.00002679678,0.003025088,0.00002785149,0.00001320765,0.00003082103,0.0001821349,0.00000597027,0.00004628387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009008076,"threshold_uncertainty_score":0.04763985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005423504622291756,"score_gpt":0.2297428841166173,"score_spread":0.2243193794943255,"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."}}