A systematic review of the benefits and harms of dipeptidyl peptidase‐4 inhibitor for chronic kidney disease
Bibliographic record
Abstract
INTRODUCTION: Diabetes mellitus (DM) is the leading cause of chronic kidney disease (CKD) and the optimal glycemic control is key to delay the progression of the disease and prevent major complications. Dipeptidyl peptidase-4 (DPP-4) inhibitors have emerged as a promising therapeutic option. However, the benefits and harms of the treatment have yet to be clarified for diabetic patients with CKD. METHODS: Type 2 diabetic patients with moderate to severe CKD including end-stage renal disease were eligible and randomized controlled trials comparing DPP-4 inhibitors with no treatment or placebo or other antihyperglycemic agents were included. A systematic electronic search was conducted through the Medline Ovid, EMBASE, Cochrane Central Register of Controlled Trials and ClinicalTrials.gov. FINDINGS: between DPP-4 inhibitors and placebo ranged from -0.60% to -0.42%. The odds ratio of hypoglycemia, mortality and severe adverse effects due to all types of DPP-4 inhibitors were 1.35 (95% CI: 0.98-1.84), 0.88 (95% CI: 0.42-1.86) and 0.86 (95% CI: 0.65-1.15), respectively while that due to DPP-4 inhibitors with renal clearance were 1.40 (95% CI: 0.87 to 2.24), 0.85 (95% CI: 0.35 to 2.04) and 0.91 (95% CI: 0.63 to 1.32), respectively. DISCUSSION: DPP-4 inhibitors demonstrated beneficial effects on the glycemic control for diabetic patients with CKD without causing any additional adverse effects. However, a definitive conclusion has yet to be drawn due to serious methodological problems and a small number of studies.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".