Clinical interpretation of serum hepcidin-25 in inflammation and renal dysfunction
Bibliographic record
Abstract
Introduction: Hepcidin is a hormone that regulates systemic iron homeostasis. Serum hepcidin levels are under the influence of various stimuli, particularly inflammation and renal dysfunction. The measurement of hepcidin in circulation is a potentially useful clinical tool in the diagnosis, monitoring and treatment of iron metabolism disorder, although clinical interpretation of hepcidin level remains difficult. We evaluated he diagnostic potential and limitations of hepcidin-25 by investigating its relationship with iron and hematological indices, inflammation, and renal dysfunction. Methods: This retrospective study included 220 adult patients not requiring dialysis. Variations of biologically active hepcidin-25 were examined using a mass spectrometry-based assay in various inflammatory and renal states. The log[hepcidin]:log[ferritin] ratio was calculated as an hepcidin index. Results: . The log[hepcidin]:log[ferritin] index normalized hepcidin levels. Patients with iron deficiency have a notably lower index when compared to controls (-0.66 vs 0.3). Conclusion: Severe renal dysfunction (eGFR < 30) affected hepcidin-25 expression and clearance to variable degree between individuals. Although, hepcidin-25 testing is not warranted in patients with infection, inflammatory autoimmune conditions (CRP > 10 mg/l) and/or severe renal dysfunction (eGFR < 30), the hepcidin index may serve as a potential biomarker for iron deficiency in complex cases.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".