Can Pentoxifylline be used as Adjunct Therapy to ACE Inhibitors and ARBs in Preserving Kidney Function?
Bibliographic record
Abstract
PURPOSE: To determine if there is sufficient evidence to recommend the addition of pentoxifylline to standard ACE inhibitor and ARB therapy in chronic kidney disease patients to reduce proteinuria and preserve kidney function. METHODS: A search of the literature was conducted using the PubMed.gov and ClinicalTrials.gov search engines and the search terms "pentoxifylline renoprotection", "pentoxifylline CKD", and "pentoxifylline nephropathy". RESULTS were limited to studies in human subjects and published in the English language. No date range was specified. Studies focused on the effects of pentoxifylline on drug induced nephropathy were excluded. RESULTS: Nine relevant articles were retrieved and evaluated. The two main populations studied were patients with chronic kidney disease (CKD) and patients with CKD and comorbid type 2 diabetes. Six of the nine studies reported a significant reduction in proteinuria in pentoxifylline treated patients. Four studies reported a significant change in estimated glomerular filtration rate (eGFR). CONCLUSION: Addition of pentoxifylline to ACE inhibitor and ARB therapy may improve proteinuria in CKD patients. There is conflicting evidence as to whether pentoxifylline will improve kidney function as measured by eGFR. KEY WORDS: pentoxifylline renoprotection, pentoxifylline CKD , pentoxifylline nephropathy.This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".