{"id":"W4408967744","doi":"10.1016/s0735-1097(25)01139-8","title":"LEVERAGING CMR-BASED PHENOTYPES TO SUPPORT AI-AUGMENTED DIABETIC CARE IN PATIENTS WITH CARDIOVASCULAR DISEASE","year":2025,"lang":"en","type":"article","venue":"Journal of the American College of Cardiology","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Medicine; Disease; Phenotype; Clinical phenotype; Internal medicine; Diabetes mellitus; Intensive care medicine; Cardiology; Endocrinology; Genetics; Gene","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":[0.002058823,0.0004519407,0.0005904895,0.001194926,0.000232963,0.002343825,0.000515616,0.0005503692,0.002040507],"category_scores_gemma":[0.01816415,0.0001532903,0.0005134289,0.0008406462,0.0001332548,0.0008249584,0.0007840444,0.001323316,0.0004379489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004230856,"about_ca_system_score_gemma":0.0007696343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003002021,"about_ca_topic_score_gemma":0.006018414,"domain_scores_codex":[0.9988759,0.0006028709,0.0001338187,0.0001451854,0.000149016,0.00009315537],"domain_scores_gemma":[0.9942449,0.003243955,0.0009574766,0.0002384828,0.0007453444,0.0005698633],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001989794,0.001676039,0.7492049,0.0002310501,0.0005989099,0.0004027933,0.0002097917,0.007595979,0.001139904,0.001248167,0.009894574,0.2258081],"study_design_scores_gemma":[0.0004744573,0.002095889,0.7927134,0.0008187551,0.001622502,0.0006638082,0.001085729,0.1722364,0.00315879,0.01191812,0.01305662,0.0001555993],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9297318,0.006415252,0.02021058,0.01458568,0.000814377,0.0002918142,0.005602673,0.0004341286,0.02191371],"genre_scores_gemma":[0.9895427,0.000761385,0.007000883,0.000899226,0.0002962786,0.00006413752,0.001107962,0.00001695674,0.0003103419],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003002021,"threshold_uncertainty_score":0.01088822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05280901890461768,"score_gpt":0.3257254568959783,"score_spread":0.2729164379913606,"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."}}