Effect of iron supplementation on iron stores and total body iron after whole blood donation
Bibliographic record
Abstract
BACKGROUND: Understanding the effect of blood donation and iron supplementation on iron balance will inform strategies to manage donor iron status. STUDY DESIGN AND METHODS: A total of 215 donors were randomized to receive ferrous gluconate daily (37.5 mg iron) or no iron for 24 weeks after blood donation. Iron stores were assessed using ferritin and soluble transferrin receptor. Hemoglobin (Hb) iron was calculated from total body Hb. Total body iron (TBI) was estimated by summing iron stores and Hb iron. RESULTS: At 24 weeks, TBI in donors taking iron increased by 281.0 mg (95% confidence interval [CI], 223.4-338.6 mg) compared to before donation, while TBI in donors not on iron decreased by 74.1 mg (95% CI, -112.3 to -35.9; p < 0.0001, iron vs. no iron). TBI increased rapidly after blood donation with iron supplementation, especially in iron-depleted donors. Supplementation increased TBI compared to controls during the first 8 weeks after donation: 367.8 mg (95% CI, 293.5-442.1) versus -24.1 mg (95% CI, -82.5 to 34.3) for donors with a baseline ferritin level of not more than 26 ng/mL and 167.8 mg (95% CI, 116.5-219.2) versus -68.1 mg (95% CI, -136.7 to 0.5) for donors with a baseline ferritin level of more than 26 ng/mL. A total of 88% of the benefit of iron supplementation occurred during the first 8 weeks after blood donation. CONCLUSION: Donors on iron supplementation replaced donated iron while donors not on iron did not. Eight weeks of iron supplementation provided nearly all of the measured improvement in TBI. Daily iron supplementation after blood donation allows blood donors to recover the iron loss from blood donation and prevents sustained iron deficiency.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".