{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008882919,0.0005958293,0.001076056,0.0003372173,0.0004018581,0.0002131184,0.0002625801,0.0001518233,0.000007464054],"category_scores_gemma":[0.0002626631,0.0005994436,0.001359716,0.0004204314,0.00007846875,0.00002852029,0.0003037459,0.0002595267,0.00001956107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001340406,"about_ca_system_score_gemma":0.0000662993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001432657,"about_ca_topic_score_gemma":0.000006001307,"domain_scores_codex":[0.9960717,0.0008882065,0.0005970516,0.001267983,0.0005574345,0.000617607],"domain_scores_gemma":[0.9982651,0.00005531577,0.000103206,0.001101818,0.0002317257,0.0002427692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005912695,0.0001873443,0.9265881,0.0001047291,0.005539463,0.00002827307,0.0007126723,0.002844364,0.03794812,0.00002147537,0.00009508758,0.02587129],"study_design_scores_gemma":[0.003528675,0.0001298275,0.963426,0.0001597745,0.003441212,0.00001137964,0.001197501,0.003329549,0.006309861,0.00001499734,0.01746964,0.0009815872],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9524136,0.03421402,0.009499887,0.0006666051,0.0006309155,0.002010786,0.00003706878,0.0001752286,0.0003518545],"genre_scores_gemma":[0.9974852,0.000738812,0.0003875093,0.0002437985,0.0002191198,0.0005616372,0.0001392619,0.00009856592,0.0001261031],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04507155,"threshold_uncertainty_score":0.9996457,"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."}}