{"id":"W4308297817","doi":"10.1016/j.inffus.2022.10.019","title":"GRACE PLUS: A data fusion-based approach to improve GRACE score in the risk assessment of Acute Coronary Syndrome","year":2022,"lang":"en","type":"article","venue":"Information Fusion","topic":"Acute Myocardial Infarction Research","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Fundação para a Ciência e a Tecnologia; Ministério da Ciência, Tecnologia e Ensino Superior; Canadian Thoracic Society","keywords":"Interpretability; Medicine; Acute coronary syndrome; Framingham Risk Score; Coronary artery disease; Risk stratification; Consistency (knowledge bases); Risk assessment; Disease; Intensive care medicine; Internal medicine; Artificial intelligence; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002391007,0.0001553046,0.0002952332,0.0004981506,0.0003305734,0.00004501986,0.0007133597,0.00006823014,0.0002075509],"category_scores_gemma":[0.0001728521,0.0001151734,0.00008239164,0.001107055,0.00005635523,0.0006423123,0.001070925,0.0007213856,0.00003512679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003677787,"about_ca_system_score_gemma":0.0005442344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004953733,"about_ca_topic_score_gemma":0.000003034148,"domain_scores_codex":[0.9970213,0.0002986532,0.0006256726,0.0002392396,0.00155094,0.0002641649],"domain_scores_gemma":[0.997992,0.000169542,0.0003019424,0.001223548,0.0002026204,0.0001103237],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01051217,0.005215438,0.1948654,0.002120009,0.001490337,0.000414664,0.03169529,0.1437621,0.01250725,0.002627739,0.4812585,0.1135312],"study_design_scores_gemma":[0.004627365,0.001754505,0.3990551,0.00007764428,0.0002119519,0.0004177819,0.006925144,0.5271135,0.00009910847,0.00003843346,0.05938192,0.0002975625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9590315,0.00003449614,0.01907969,0.009404105,0.0003577931,0.004646307,0.002155988,0.0000896124,0.005200575],"genre_scores_gemma":[0.9863536,0.00004804752,0.003700089,0.00536894,0.00001813359,0.0003757468,0.004032101,0.00001304248,0.00009033327],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4218765,"threshold_uncertainty_score":0.4696636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03594911129743265,"score_gpt":0.3294071358462499,"score_spread":0.2934580245488172,"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."}}