Albuminuria, proteinuria, and dipsticks: novel relationships and utility in risk prediction
Bibliographic record
Abstract
PURPOSE OF REVIEW: Albuminuria is associated with progression of kidney disease and is the accepted gold standard for screening, staging, and prognostication of chronic kidney disease. This review focuses on current literature that has explored applications of albuminuria as a surrogate outcome, variable used in kidney failure risk prediction for novel populations, and variable that may be predicted by other proteinuria measures. RECENT FINDINGS: Change in albuminuria shows promise as a surrogate outcome for kidney failure, which may have major implications for trial design and conduct. The kidney failure risk equation (KFRE) has been validated extensively to date and has now been applied to pediatric patients with kidney disease, advanced age, different causes of kidney disease, various countries, and those with prior kidney transplants. As albumin-to-creatinine ratios (ACRs) are not always available to clinicians and researchers, two recent studies have independently developed equations to estimate ACR from other proteinuria measures. SUMMARY: The utility of albuminuria and the KFRE continues to grow in novel populations. With the ability to convert more widely available (and inexpensive) proteinuria measures to ACR estimates, the prospect of incorporating kidney failure risk prediction into routine care within economically challenged healthcare jurisdictions may finally be realized.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".