The Gomez equations and renal hemodynamic function in kidney disease research
Bibliographic record
Abstract
Diabetic kidney disease (DKD) remains the leading cause of end-stage renal disease. A major challenge in preventing DKD is the difficulty in identifying high-risk patients at an early, pre-clinical stage. Albuminuria and eGFR as measures of renal function in DKD research and clinical practice are limited by regression of one-third of patients with microalbuminuria to normoalbuminuria and eGFR is biased and imprecise in the normal-elevated range. Moreover, existing methods that are used to assess renal function do not give detailed insight into the location of the renal hemodynamic effects of pharmacological agents at the segmental level. To gain additional information about the intrarenal circulation in-vivo in humans, mathematical equations were developed by Gomez et al in the 1950s. These equations used measurements of GFR, renal blood flow (RBF), effective renal plasma flow (ERPF), renal vascular resistance (RVR), hematocrit and serum protein to calculate afferent and efferent arteriolar resistances, glomerular hydrostatic pressure and filtration pressure. Although indirect and based on physiological assumptions, these techniques have the potential to improve researchers' ability to identify early pre-clinical changes in renal hemodynamic function in patients with a variety of conditions including DKD, thereby offering tremendous potential in mechanistic human research studies. In this review, we focus on the application of Gomez' equations and summarize the potential and limitations of this technique in DKD research. We also summarize illustrative data derived from Gomez' equations in patients with type 1 (T1D) and type 2 diabetes (T2D) and hypertension.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".