Functional iron deficiency in hemodialysis patients with high ferritin
Bibliographic record
Abstract
Although functional iron deficiency (FID) may be present in hemodialysis (HD) patients with high serum ferritin levels (>800 ng/mL), current protocols often preclude the use of intravenous (IV) iron in these patients. However, it has not been demonstrated that iron supplementation during erythropoietin therapy is ineffective or unsafe in increasing hemoglobin (Hb) levels in patients with high serum ferritin. This report describes the hematologic efficacy and safety of ferric gluconate (FG) therapy in patients with serum ferritin >800 ng/mL. A retrospective analysis was performed on HD patients at a single California dialysis center from January 1 to December 31, 2003. Patients classified as having high ferritin levels (serum ferritin >800 ng/mL on at least 66% of routine monthly measurements and transferrin saturation [TSAT] <25% on at least 1 occasion) were stratified as follows: patients in Group I were suspected of having FID and received FG > or =250 mg IV over a 3-month period when Hb was <11 g/dL, and patients in Group II were thought not to have FID and received <250 mg FG over a 3-month period. Both groups received standard recombinant human erythropoietin therapy as per the unit's protocol. Of 496 patients, 95 exhibited high ferritin and of these, 39 patients had sufficient data for analysis. Group I patients (n=14) showed a significant increase in Hb levels compared with Group II (n=25). There was no increase in ferritin levels in response to iron administration. No significant differences in hospitalizations or infections were observed between groups. Hemodialysis patients with high ferritin levels may have FID, and IV iron therapy safely improves FID in some patients. A larger randomized trial examining the optimal management of iron administration in HD patients with high ferritin levels is warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| 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".