{"id":"W3110432791","doi":"10.23736/s0392-9590.20.04538-1","title":"Cardiovascular disease and stroke risk assessment in patients with chronic kidney disease using integration of estimated glomerular filtration rate, ultrasonic image phenotypes, and artificial intelligence: a narrative review","year":2021,"lang":"en","type":"review","venue":"International Angiology","topic":"Cardiovascular Health and Disease Prevention","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Medicine; Kidney disease; Disease; Stroke (engine); Renal function; Risk factor; Biomarker; Risk assessment; Intensive care medicine; Internal medicine; Cohort; Cardiology; Framingham Risk Score; Coronary artery disease","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005886566,0.0003521088,0.001513354,0.000278888,0.0000606447,0.00002741032,0.0000790486,0.0001302358,0.00008018913],"category_scores_gemma":[0.001022688,0.0002756289,0.0006739729,0.000296559,0.0001565153,0.000162173,0.00005826432,0.0003502773,0.000001566876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004232587,"about_ca_system_score_gemma":0.001805976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001150824,"about_ca_topic_score_gemma":0.00001483049,"domain_scores_codex":[0.9971787,0.0007699156,0.0008152205,0.0006185286,0.0004065306,0.000211053],"domain_scores_gemma":[0.9982585,0.0001113888,0.0004305568,0.0003487177,0.0004836659,0.0003671922],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0009053339,0.0007592626,0.01135706,0.04688543,0.00744443,0.0002011074,0.00009198175,0.0002342664,0.00001414318,0.0004738978,0.00005288905,0.9315802],"study_design_scores_gemma":[0.008581107,0.001334112,0.4001348,0.4387235,0.08301631,0.0002396176,0.0002233835,0.01977637,0.000009895273,0.001582282,0.04349006,0.002888619],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.01186156,0.9796194,0.005590844,0.00008188831,0.0001707883,0.001947847,0.0007012152,0.00001506288,0.00001138476],"genre_scores_gemma":[0.0147441,0.9770647,0.001054845,0.00005951064,0.0001288426,0.0001815766,0.006732252,0.00002815405,0.000005991276],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9286916,"threshold_uncertainty_score":0.9999696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03042586835638873,"score_gpt":0.3573235887376038,"score_spread":0.3268977203812151,"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."}}