Prognostic utility of estimated albumin excretion rate in chronic kidney disease: results from the Study of Heart and Renal Protection
Bibliographic record
Abstract
Background: Estimated albumin excretion rate (eAER) provides a better estimate of 24-h albuminuria than albumin:creatinine ratio (ACR). However, whether eAER is superior to ACR in predicting end-stage renal disease (ESRD), vascular events (VEs) or death is uncertain. Methods: The prognostic utility of ACR and eAER (estimated from ACR, sex, age and race) to predict mortality, ESRD and VEs was compared using Cox proportional hazards regression among 5552 participants with chronic kidney disease in the Study of Heart and Renal Protection, who were not on dialysis at baseline. Results: During a median follow-up of 4.8 years, 1959 participants developed ESRD, 1204 had a VE and 1130 died (641 from a non-vascular, 369 from a vascular and 120 from an unknown cause). After adjustment for age, sex and eGFR, both ACR and eAER were strongly and similarly associated with ESRD risk. The average relative risk (RR) per 10-fold higher level was 2.70 (95% confidence interval 2.45-2.98) for ACR and 2.67 (2.43-2.94) for eAER. Neither ACR nor eAER provided any additional prognostic information for ESRD risk over and above the other. For VEs, there were modest positive associations between both ACR and eAER and risk [adjusted RR per 10-fold higher level 1.37 (1.22-1.53) for ACR and 1.36 (1.22-1.52) for eAER]. Again, neither measure added prognostic information over and above the other. Similar results were observed when ACR and eAER were related to vascular mortality [RR per 10-fold higher level: 1.64 (1.33-2.03) and 1.62 (1.32-2.00), respectively] or to non-vascular mortality [1.53 (1.31-1.79) and 1.50 (1.29-1.76), respectively]. Conclusions: In this study, eAER did not improve risk prediction of ESRD, VEs or mortality.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".