PMID: 6943836 opkov J, Padrov J, Romnkov V, Havlovicov M, Balakov M, Zelinov M, Vejvalkov , Simandlov M, tpnkov J, Honov V, Kantorov E, Kekov G, Pospilov J, Boday A, Meszarosov AU, Turnovec M, Votpka P, Likov P, Kremlkov PR
Reasons to avoid automatic substitution Head-to-head data are limited
Within the same calibration and reclassification architecture, clinicians may layer additional modalitiessuch as imaging features, circulating biomarkers, and wearable-derived metricsto refine individual risk prediction and provide early warnings during longitudinal follow-up ( 3 Emerging technologies for early risk stratification of DKD To extend the detection window of diabetic kidney disease (DKD), it is essential to enhance the current KDIGO risk stratification frameworkbased on eGFR, uACR, and the Kidney Failure Risk Equation (KFRE)through the systematic integration of molecular, tissue, and continuous exposure-level data
We also recognize that weight management does not exist in isolation