Statin use is associated with lower inflammation and erythropoietin responsiveness index in hemodialysis patients
Bibliographic record
Abstract
Patients with end-stage renal disease are prone to inflammation and inflammation is related to erythropoietin-stimulating agent hyporesponsiveness and mortality in this population. Statins have been demonstrated to reduce cardiovascular mortality in selected populations of end-stage renal disease patients. These drugs have pleiotrophic effects such as anti-inflammation. In this retrospective analysis, we determined whether the use of statins improves inflammation and inflammation-related anemia in a cohort of hemodialysis patients. Data were analyzed from Fresenius Medical Care Dialysis Clinics in Turkey between 2005 and 2007. Seventy prevalent hemodialysis patients who were on statins at the start of the study and have been on statins during follow-up (statin users) and 1293 patients who were not on statin at the start of the study and had never been prescribed any lipid-modifying drugs during follow-up (statin nonusers) were included in the study. High-sensitive C-reactive protein levels were significantly decreased in statin users (1.50±1.49 vs. 1.33±1.11 mg/L, P=0.05) compared with nonusers (1.93±3.22 vs. 2.05±2.77 mg/L). Hemoglobin levels and the rate of erythropoietin-stimulating agent users were similar. However, the prescribed erythropoietin-stimulating agent dose (31.6±27.5 vs. 47.3±45.2 U/kg/week, P<0.05) and the erythropoietin response index (2.90±2.73 vs. 4.51±4.48 U/kg/week/Hb, P=0.001) were lower in statin users compared with statin nonusers. On stepwise multiple regression analysis, gender, high-sensitive C-reactive protein, duration of hemodialysis, serum ferritin, and statin use were independent determinants of the erythropoietin responsiveness index. Our results suggest that statin treatment leads to lower inflammation and improves hematopoiesis in hemodialysis patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".