{"id":"W7117406614","doi":"10.1016/j.kint.2025.11.021","title":"Utilizing risk prediction models for older patients with chronic kidney disease","year":2025,"lang":"en","type":"article","venue":"Kidney International","topic":"Chronic Kidney Disease and Diabetes","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Seven Oaks General Hospital","funders":"Janssen Pharmaceuticals; Canadian Institutes of Health Research; National Institutes of Health; Otsuka Pharmaceutical; Akebia Therapeutics; Novo Nordisk; Research Manitoba; CSL Behring; AstraZeneca; Eli Lilly and Company; Kidney Foundation of Canada; Amgen","keywords":"Kidney disease; Nephrology; Risk assessment; Predictive modelling; Risk model; Chronic renal failure; Disease; Kidney","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01225244,0.001379123,0.001306029,0.002911738,0.0005932836,0.003949479,0.001228833,0.001024708,0.002284308],"category_scores_gemma":[0.05792506,0.0003946606,0.002072892,0.002013893,0.0004159695,0.002459486,0.001923026,0.002686346,0.0007324364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001678142,"about_ca_system_score_gemma":0.002682461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01251761,"about_ca_topic_score_gemma":0.01366159,"domain_scores_codex":[0.995554,0.002582862,0.000507076,0.0003537729,0.0008431572,0.0001590126],"domain_scores_gemma":[0.9741087,0.01932413,0.002199247,0.0006286178,0.003224957,0.0005143867],"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":[0.0006240976,0.0004055896,0.2370065,0.002122385,0.00257206,0.000767157,0.00129878,0.2200819,0.0006402938,0.03648325,0.03341497,0.4645831],"study_design_scores_gemma":[0.00007845567,0.0004127813,0.02894453,0.003132283,0.000982283,0.0008139358,0.0006827172,0.8075307,0.001043603,0.1235051,0.03262253,0.000251062],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1431584,0.06505247,0.732752,0.02564217,0.001782784,0.001226075,0.006981886,0.002268427,0.0211359],"genre_scores_gemma":[0.7134195,0.02562339,0.2496047,0.002081913,0.0008586032,0.0007278064,0.005191088,0.0002455908,0.002247468],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01251761,"threshold_uncertainty_score":0.06479782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008225359344565725,"score_gpt":0.2565456299823451,"score_spread":0.2483202706377794,"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."}}