Hemochromatosis gene mutations and treatment of anemia in patients on hemodialysis
Bibliographic record
Abstract
Hemochromatosis causes iron overload by enhanced intestinal absorption. This study examined erythropoietin and intravenous (i.v.) iron requirements in hemodialysis (HD) patients with HFE mutations. Patients on HD for > 90 days with no cause of anemia except chronic kidney disease were tested for HFE mutations (H63D and C282Y). Intravenous iron and erythropoietin doses were adjusted to achieve recommended targets. Monthly hemoglobin (Hb), ferritin, mean corpuscular volume, mean cell hemoglobin, erythropoietin, and i.v. iron doses for 3 consecutive months were averaged. Of 172 patients, 71 (41.3%) had > or = 1 HFE mutation: 24 (14%) C282Y heterozygotes, 40 (23.3%) H63D heterozygotes, 5 compound heterozygotes, and 2 homozygotes. Comparing patients with > or = 1 HFE mutation to those without mutations showed no significant difference in Hb or serum ferritin. There was a trend toward lower median weekly erythropoietin dose in patients with > or = 1 HFE mutation (94.0 vs. 135.4 U/kg body weight; P=0.13). There was no difference in median weekly i.v. iron dose (1.0 vs. 0.9 mg/kg body weight; P=0.56). Comparing the 30 patients with a C282Y mutation to patients without HFE mutations produced similar results. Comparing the 47 patients with an H63D mutation, with those without HFE mutations, no discernable trend was observed. In this study, patients with HFE gene mutations on HD for established renal failure do not require less iron supplementation to achieve recommended Hb targets. We observed a trend toward lower erythropoietin requirement in patients possessing C282Y mutations. Larger studies may clarify the role of HFE mutations, regulators of iron metabolism and erythropoiesis in chronic kidney disease.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".