Effects of Lowering LDL Cholesterol on Progression of Kidney Disease
Bibliographic record
Abstract
Lowering LDL cholesterol reduces the risk of developing atherosclerotic events in CKD, but the effects of such treatment on progression of kidney disease remain uncertain. Here, 6245 participants with CKD (not on dialysis) were randomly assigned to simvastatin (20 mg) plus ezetimibe (10 mg) daily or matching placebo. The main prespecified renal outcome was ESRD (defined as the initiation of maintenance dialysis or kidney transplantation). During 4.8 years of follow-up, allocation to simvastatin plus ezetimibe resulted in an average LDL cholesterol difference (SEM) of 0.96 (0.02) mmol/L compared with placebo. There was a nonsignificant 3% reduction in the incidence of ESRD (1057 [33.9%] cases with simvastatin plus ezetimibe versus 1084 [34.6%] cases with placebo; rate ratio, 0.97; 95% confidence interval [95% CI], 0.89 to 1.05; P=0.41). Similarly, allocation to simvastatin plus ezetimibe had no significant effect on the prespecified tertiary outcomes of ESRD or death (1477 [47.4%] events with treatment versus 1513 [48.3%] events with placebo; rate ratio, 0.97; 95% CI, 0.90 to 1.04; P=0.34) or ESRD or doubling of baseline creatinine (1189 [38.2%] events with treatment versus 1257 [40.2%] events with placebo; rate ratio, 0.93; 95% CI, 0.86 to 1.01; P=0.09). Exploratory analyses also showed no significant effect on the rate of change in eGFR. Lowering LDL cholesterol by 1 mmol/L did not slow kidney disease progression within 5 years in a wide range of patients with CKD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".