{"id":"W4414015798","doi":"10.11159/icbes25.177","title":"Automated Risk Stratification of Peripheral Artery Disease via Optimized Volume Rendering and Vascular Biomarkers","year":2025,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Peripheral Artery Disease Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Commission","keywords":"Risk stratification; Peripheral; Volume rendering; Rendering (computer graphics); Computer science; Stratification (seeds); Arterial disease; Vascular disease; Cardiology; Medicine; Internal medicine; Artificial intelligence; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001127369,0.0009313577,0.0008444738,0.001463111,0.0002570996,0.002149703,0.0008141404,0.0007588121,0.001106362],"category_scores_gemma":[0.004080608,0.0005744523,0.0008768017,0.0005807174,0.0004138144,0.0006785684,0.001147674,0.0008607608,0.000546158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003845493,"about_ca_system_score_gemma":0.0009665834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002407153,"about_ca_topic_score_gemma":0.002850289,"domain_scores_codex":[0.9993788,0.0002317753,0.00003250472,0.0001005068,0.0002108126,0.00004555178],"domain_scores_gemma":[0.9993621,0.0002902896,0.0000920442,0.00008322536,0.0001231994,0.00004916126],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007195385,0.0003195384,0.01331749,0.0002638421,0.0002305661,0.0007479752,0.000542315,0.3179326,0.1144294,0.009935509,0.007496316,0.5340649],"study_design_scores_gemma":[0.00002967725,0.00008005999,0.002504572,0.00003486944,0.00004809638,0.0004476866,0.00003615745,0.9759627,0.0113404,0.006932223,0.002524878,0.00005872841],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02777185,0.0005204455,0.9678497,0.0002503561,0.00004663822,0.00008558569,0.0001786588,0.002204902,0.001091894],"genre_scores_gemma":[0.4130802,0.0008188243,0.5833097,0.0002605754,0.0001316192,0.0001260094,0.0004555201,0.0005971171,0.001220448],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002407153,"threshold_uncertainty_score":0.005962133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00458657926622536,"score_gpt":0.2085147039596449,"score_spread":0.2039281246934195,"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."}}