The effect of statins on preservation of kidney function in patients with coronary artery disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: This paper outlines evidence for the putative renal benefits of statins in people with vascular disease. RECENT FINDINGS: The Greek Atorvastatin and Coronary Heart Disease study showed a modest improvement in kidney function over 4 years among 800 atorvastatin recipients (12%), significantly better than the decrease in kidney function (4%) in 800 placebo recipients. A secondary analysis of the Cholesterol and Recurrent Events trial suggested that pravastatin reduced the rate of kidney function loss to a greater extent in participants with dipstick-positive proteinuria (P < 0.001) and lower levels of renal function at baseline (P = 0.04). A larger post-hoc analysis from this group found that pravastatin modestly reduced the risk of acute renal failure (RR 0.60, 95% CI 0.41-0.86), but not the risk of a 25% decline in kidney function from baseline (RR 0.94, 95% CI 0.88-1.01). In the group with lower baseline kidney function (glomerular filtration rate <60 ml/min/1.73 m(2)) and proteinuria on dipstick urinalysis (n = 249), pravastatin recipients were less likely to experience a 25% or greater decrease in glomerular filtration rate (12.5% versus 19.9%) or acute renal failure (3.2% versus 8.7%). SUMMARY: Statins may reduce the rate of kidney function loss in people with cardiovascular disease, although the clinical significance of this effect is unclear. Future studies are required before statins can be recommended solely to protect renal function.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".