Bibliographic record
Abstract
BACKGROUND: Hypertension and diabetes are common risk factors for nephropathy as well as for neuropathy, retinopathy, cardiovascular disease, and cerebrovascular disease. Diabetic nephropathy occurs in 20% to 40% of patients with type 2 diabetes mellitus and is the single most important cause of end-stage renal disease (ESRD) worldwide, accounting for 40% to 45% of new cases in the United States. The incidence of ESRD is predicted to increase as the prevalence of type 2 diabetes mellitus and obesity continue to increase. METHODS: Clinical data from the recent classes of antihypertensive agents are reviewed in the context of hypertension reduction guidelines and prevention of diabetic nephropathy. RESULTS: Numerous clinical trials have demonstrated that angiotensin receptor blockers (ARBs) are safe and effective antihypertensive treatments that slow the progression of renal disease in people with diabetes and/or hypertension, and macroalbuminuria. CONCLUSION: The tolerable adverse event profile of ARBs and their renoprotective benefits beyond blood pressure reduction make ARBs a useful first-line treatment in people with, or at risk of developing, renal disease. As the incidence of obesity-related cardiovascular disease and renal risk factors continues to grow, future studies are required to directly assess the renoprotective effects of ARBs in overweight or obese patient subgroups. Because renin angiotensin system (RAS) inhibitors target the key mechanisms underlying these conditions, they may be particularly beneficial for the prevention of ESRD in the growing group of patients with obesity-related hypertension and the metabolic syndrome.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".