Blood donor populations reveal a clear association between ferritin and change in haemoglobin levels
Bibliographic record
Abstract
Many blood establishments worldwide monitor serum ferritin alongside mandatory haemoglobin (Hb) screening to better protect donors from iron deficiency and anaemia. However, the relationship between ferritin and Hb, and the ferritin level that indicates iron deficiency, remains unclear. Whole blood donation results in significant iron loss, and repeated donations can deplete iron stores. This study analysed over 1 million whole-blood donations from four countries to explore the association between Hb change and ferritin levels. Hb change was defined relative to a donor's initial Hb level. A consistent two-phase relationship emerged: At low ferritin levels, Hb change is linearly associated with log ferritin; above a certain threshold, this association disappears as donors recover their reference Hb. The transition point and slope of this association differ by population. These results suggest that ferritin thresholds for identifying limited Hb recovery are not universal but population-specific, influenced by biological and procedural differences, including ferritin assay variability. While the overall pattern is consistent, the absence of standardized procedures and assays limits the ability to define global ferritin thresholds for donor care. This underscores the importance of localized approaches to ferritin-based donor management and the need for harmonized methodologies across blood services.
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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 | 0.003 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".