Estimated glomerular filtration rate and albuminuria
Bibliographic record
Abstract
PURPOSE OF REVIEW: To describe the relationship between estimated glomerular filtration rate (eGFR), albuminuria, and important outcomes for patients with chronic kidney disease (CKD). The first part of the review presents the evidence linking eGFR and albuminuria to important clinical outcomes, and the second part highlights the importance of these risk relationships across multiple subgroups and in clinical risk prediction. RECENT FINDINGS: Investigators have used data from large population-based cohort studies and conducted collaborative meta-analyses to definitively establish the relationship between eGFR, albuminuria, and adverse clinical outcomes. Recent systematic reviews have also highlighted the importance of these variables in predicting the risk of kidney failure and all-cause mortality. SUMMARY: eGFR and albuminuria are important independent risk factors for kidney failure, acute kidney injury, and all-cause or cardiovascular mortality. These relationships are independent of age, sex, race, or ethnicity. eGFR and albuminuria can be combined with other demographic variables to accurately predict the risk of kidney failure and should be measured concurrently to determine diagnosis, staging, and prognosis in patients with CKD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".