{"id":"W4409893307","doi":"10.3390/a18050257","title":"Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning","year":2025,"lang":"en","type":"article","venue":"Algorithms","topic":"Cardiac Valve Diseases and Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"NOSM University; Science North; Laurentian University","funders":"","keywords":"Artificial intelligence; Internal medicine; Computer science; Machine learning; Medicine; Cardiology","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":[0.0003174973,0.000130196,0.0003508898,0.0001487417,0.00007760146,0.00002120314,0.00008202474,0.00008735577,0.00003285949],"category_scores_gemma":[0.0001850065,0.000113319,0.0002675169,0.0002710193,0.00003332599,0.00009376961,0.0001248393,0.0003817395,0.00005567057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008969539,"about_ca_system_score_gemma":0.00008274406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008700736,"about_ca_topic_score_gemma":0.000003656271,"domain_scores_codex":[0.9987112,0.0001116822,0.0003628057,0.0004183336,0.0001962745,0.0001997157],"domain_scores_gemma":[0.9991459,0.0001032591,0.00007853119,0.0005398925,0.00002811484,0.0001043597],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001552625,0.0003282659,0.9678255,0.00001278927,0.001076686,0.0002696296,0.00001372031,0.00003445316,0.000005852879,0.000003743387,0.00005610313,0.03021804],"study_design_scores_gemma":[0.002651797,0.0004614231,0.935084,0.0001784905,0.00178135,0.00002727042,0.00001466757,0.05950789,0.000003973191,0.00003628427,0.0001844077,0.00006847978],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974332,0.000503442,0.000509681,0.00006962418,0.0004983759,0.0003109539,0.0003123331,0.00006212034,0.0003002219],"genre_scores_gemma":[0.9975216,0.0003902518,0.0007902919,0.0001068098,0.00008273638,0.000008628502,0.0005086949,0.00001769522,0.000573339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05947344,"threshold_uncertainty_score":0.4621016,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04771946088566265,"score_gpt":0.4229917483475785,"score_spread":0.3752722874619159,"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."}}