{"id":"W4409185257","doi":"10.53759/7669/jmc202505098","title":"Advancing Health Diagnostics: AI-Powered CVD-REF Framework for Precise and Early Risk Assessment","year":2025,"lang":"en","type":"article","venue":"Journal of Machine and Computing","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Risk assessment; Risk analysis (engineering); Computer science; Medicine; Systems engineering; Engineering; Computer security","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.002904568,0.00015439,0.0005213606,0.0001792288,0.001344481,0.00003783075,0.0001367549,0.000145362,0.00001112558],"category_scores_gemma":[0.002775457,0.0001286207,0.00007453939,0.0001573222,0.00003942886,0.0001283681,0.000156142,0.001597846,0.000001101315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001767389,"about_ca_system_score_gemma":0.0006089052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007695685,"about_ca_topic_score_gemma":0.0002672224,"domain_scores_codex":[0.997281,0.0005500198,0.001306169,0.0002039577,0.000187381,0.0004714311],"domain_scores_gemma":[0.9918987,0.006120924,0.001150285,0.0001488774,0.0004451138,0.0002361374],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001134449,0.0000558685,0.7435668,0.0006479726,0.00005166626,0.000004905023,0.004074993,0.0002040616,0.000006780624,0.007211617,0.001406448,0.2426555],"study_design_scores_gemma":[0.002016513,0.002906224,0.53385,0.01615424,0.0002127983,0.00002817698,0.01526331,0.1254654,0.00006368345,0.286437,0.01704877,0.0005538804],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6166711,0.008591272,0.3603112,0.01210871,0.001456275,0.0006992245,0.00001767609,0.00002345451,0.0001210667],"genre_scores_gemma":[0.9377033,0.002497487,0.05644261,0.002705208,0.0005961838,0.000007683585,0.000001206006,0.0000180504,0.00002828093],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3210322,"threshold_uncertainty_score":0.9999557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03797665696017907,"score_gpt":0.4986130230387605,"score_spread":0.4606363660785814,"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."}}