<scp>IL</scp>‐6 is an independent risk factor for resistance to erythropoiesis‐stimulating agents in hemodialysis patients without iron deficiency
Bibliographic record
Abstract
Anemia is a common complication in dialysis patients because of their relative erythropoietin deficiency. Despite treatment with erythropoiesis-stimulating agents (ESAs), some patients experienced ESA hyporesponsiveness. We evaluated the clinical and laboratory factors that affect ESA hyporesponsiveness and investigated the relationships between hepcidin, inflammatory markers, and the iron profiles of hemodialysis patients. Sixty-eight patients receiving hemodialysis at a single institution were evaluated in a cross-sectional study. The patients were divided into tertiles based on the ESA hyporesponsiveness index (EHRI), defined as the weekly ESA dose per kilogram of body weight divided by the hemoglobin level. The mean EHRI values for each tertile were 3.3 ± 1.2 (T1), 10.2 ± 2.9 (T2), and 24.5 ± 11.6 (T3). The mean serum erythropoietin levels were significantly higher in the Q3 and Q4 groups. Thus, patients with ESA hyporesponsiveness showed relative resistance to erythropoietin therapy. In univariate and multivariate analyses, patients in the third tertile of EHRI showed significantly higher mean interleukin-6 (IL-6) levels. Serum C-reactive protein (CRP) levels showed a similar trend, but the differences were not significant. Serum hepcidin levels tended toward lower mean values in the third tertile of EHRI. No relationship was observed between hepcidin and inflammatory markers or iron status. In conclusion, IL-6, but not CRP, is a strong predictor of ESA hyporesponsiveness in hemodialysis patients who have sufficient iron. It may be difficult to use hepcidin as an independent clinical marker because of the many factors that influence it and their interactions.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".