A hepcidin‐based approach for iron therapy in hemodialysis patients: A pilot study
Bibliographic record
Abstract
INTRODUCTION: Hepcidin is a key factor that regulates iron homeostasis. In hemodialysis patients (HD), a high hepcidin level may decrease intestinal iron absorption and reduce the efficacy of Oral iron vs Intravenous iron therapy. Whether the hepcidin level in HD could guide oral iron therapy is unclear. METHODS: We report a monocentric study on nine "erythropoietin (EPO)-free" patients (without recombinant human EPO [rHU-EPO] for at least 6 months) and normal hepcidin level (<20 ng mL) during the study. After 15 days of washout, oral iron (ferrous sulfate 80 mg/day) was introduced. The primary end point was the hemoglobin response and iron store at 3 months. FINDINGS: Nine patients (8 men, 1 woman) with a median age of 62 years (range 42-79) were included. After 1 week of treatment, the median transferrin saturation index increased from 15% (range 6-61) to 34% (range 13-42), P = 0.62, reflecting intestinal absorption. The median ferritin level remained stable 80 μg/L (35-293) vs 82 μg/L (range 37-496) between M0 and M3, P = 0.43. During the 3-month study, median hemoglobin level increased from 11.5 d/dL (range10.4-13.7) to 12.8 g/dL (range 11.1-15.2), P = 0.01. No major side effects were observed. Quality of life assessed by the SF-36 criteria was similar during the 3-month study. DISCUSSION: Oral iron therapy is effective and safe in EPO-free patients with normal hepcidin levels. These findings suggest that serum hepcidin may be a marker for defining iron therapy strategies in HD patients. HD patients treated with rHU-EPO and with normal hepcidin levels could benefit from oral iron treatment.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".