Combination of Changes in Estimated GFR and Albuminuria and the Risk of Major Clinical Outcomes
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Whether combining changes in eGFR and urine albumin-to-creatinine ratio (UACR) is more strongly associated with outcomes compared with either change alone is unknown. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We analyzed 8766 patients with type 2 diabetes in the Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation Observational (ADVANCE-ON) study. Changes in eGFR and UACR (baseline to 2 years) were defined as ≥40% decrease, minor change, and ≥40% increase. The primary outcome was the composite of major macrovascular (nonfatal or fatal myocardial infarction, nonfatal or fatal stroke, or cardiovascular death), major kidney events (requirement for kidney replacement therapy or kidney death), and all-cause mortality. RESULTS: Over a median of 7.7 years of follow-up, 2191 primary outcomes were recorded. Strong linear associations between eGFR and UACR changes and subsequent risk of the outcome were observed. For eGFR, the hazard ratios were 1.58 (95% confidence interval [95% CI], 1.27 to 1.95) for a decrease ≥40% and 0.82 for an increase ≥40% (95% CI, 0.64 to 1.04) compared with minor change. For UACR, the hazard ratios were 0.96 (95% CI, 0.85 to 1.07) for a decrease ≥40% and 1.32 (95% CI, 1.19 to 1.46) for ≥40% increase compared with minor change. Compared with dual minor changes, both an eGFR decrease ≥40% and a UACR increase ≥40% had 2.31 (95% CI, 1.67 to 3.18) times the risk of the outcome, with evidence of interaction between the two markers. CONCLUSIONS: Clinically meaningful decreases in eGFR and increases in UACR over 2 years, independently and in combination, were significantly associated with higher risk of major clinical outcomes.
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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 | 0.004 | 0.009 |
| 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 | 0.001 |
| 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".