Double red blood cell donors with increased ferritin levels: a descriptive study
Bibliographic record
Abstract
BACKGROUND: One of the measures proposed to mitigate iron loss in blood donors is monitoring of their ferritin levels. Occasionally, high ferritin levels are found in monitored donors. We report the results of the clinical and laboratory investigation of 80 double red blood cell (DRBC) donors with high ferritin levels. STUDY DESIGN AND METHODS: All DRBC donors' ferritin levels were measured during each visit for a donation. Donors with high ferritin levels who agreed to participate underwent a clinical and laboratory evaluation. RESULTS: A total of 165 of 2757 DRBC donors had at least one high ferritin level. Five were already known to suffer from hemochromatosis. A full investigation was available for 80 other donors. A total of 61 of 80 donors had normalized their ferritin level at the time of their laboratory evaluation. Only 16 donors had high serum iron levels, of whom four had increased saturation index. Genetic analysis gave the following results: C282Y homozygous, two; H63D homozygous, six; C282Y/HC3D double heterozygote, six; C282Y heterozygote, six; H63D heterozygote, 19; and no mutations, 39. None of the other laboratory investigations contributed data explaining the high ferritin levels observed. CONCLUSION: In most donors with high ferritin levels, the phenomenon was transient, with normal ferritin levels found in follow-up. Less than 10% of these donors had evidence of iron overload. Only eight were homozygous for mutations associated with hemochromatosis. An extensive laboratory investigation by the treating physician should only be recommended in donors with persistently high ferritin levels.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".