{"id":"W3011285191","doi":"10.1016/s0735-1097(20)34169-3","title":"ARTIFICIAL INTELLIGENCE-ENABLED ECG ALGORITHM FOR THE SCREENING OF DIABETES","year":2020,"lang":"en","type":"article","venue":"Journal of the American College of Cardiology","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University Health Centre","funders":"","keywords":"Medicine; Diabetes mellitus; Artificial intelligence; Machine learning; Endocrinology","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":[],"consensus_categories":[],"category_scores_codex":[0.001956864,0.0001607024,0.001159788,0.00009720158,0.0004639638,0.000002413626,0.0009404662,0.0001125975,0.00003131792],"category_scores_gemma":[0.002716793,0.00009197897,0.0005888791,0.0006920018,0.0008789271,0.00006342296,0.0002153548,0.0008066141,0.00000446526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006950655,"about_ca_system_score_gemma":0.0006259131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007484161,"about_ca_topic_score_gemma":0.00002853073,"domain_scores_codex":[0.9959255,0.001293033,0.001799541,0.0001666463,0.0003466988,0.0004685073],"domain_scores_gemma":[0.9900423,0.005571628,0.002745548,0.0003474363,0.001156921,0.0001361219],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002664842,0.0000931259,0.255789,0.0006781962,0.002378675,0.00003660875,0.005535132,0.03010284,0.009035948,0.006041415,0.03578436,0.6518599],"study_design_scores_gemma":[0.0009213042,0.01310749,0.02766132,0.001657804,0.002027138,0.0001097094,0.3173046,0.4431259,0.06455153,0.04633217,0.08201543,0.001185539],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5287294,0.001190587,0.4023679,0.0612367,0.003298864,0.002263047,0.000571209,0.00002335082,0.0003189867],"genre_scores_gemma":[0.9860231,0.0001327811,0.01026008,0.001667366,0.001824241,0.00003206175,6.926307e-7,0.00002706837,0.00003263776],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6506743,"threshold_uncertainty_score":0.3750794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1337297964156142,"score_gpt":0.4182294966719018,"score_spread":0.2844997002562877,"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."}}