Iron status and risk factors for iron depletion in a racially/ethnically diverse blood donor population
Bibliographic record
Abstract
BACKGROUND: The optimal approach for reducing iron depletion (ID) in blood donors may vary depending on biologic or behavioral differences across donors. STUDY DESIGN AND METHODS: More than 12,600 successful whole blood donors were enrolled from four US blood centers for ferritin testing. The study population was enriched for racial/ethnic minorities (1605 African American, 1616 Asian, 1023 Hispanic). Subjects completed questionnaires on ID risk factors. Logistic regression identified predictors of absent iron stores (AIS; ferritin <12 ng/mL) and low ferritin (LF; ferritin <26 ng/mL). RESULTS: Across all subjects, 19% had AIS and 42% had LF, with a sharp increase in risk observed with increasing donation intensity and among women a large decrease in risk in those more than 50 years old. When other factors were controlled for, African American and Asian donors showed 20% to 25% decreased risk for AIS compared to non-Hispanic Caucasian donors, while Hispanic donors had 25% higher risk. Daily iron supplementation reduced risk for LF and AIS by 30% to 40%, respectively, while the benefit from less frequent use was lower (7%-19% protection). Regular antacid use was associated with at least 20% increment to risk. Use of oral contraceptives or estrogen in females reduced risk by 16% to 22%, while males who reported supplemental testosterone use had a 50% to 125% greater risk for LF and AIS. CONCLUSIONS: This study confirms high prevalence of LF and AIS in US donors and the principal risk factors of age, sex, and donation frequency. Additional demographic and behavioral risk factors of secondary importance might allow for refinement of ID mitigation strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".