Impact of informing donors of low ferritin results
Bibliographic record
Abstract
BACKGROUND: Informing donors of their ferritin status is one possible mitigating strategy to reduce iron deficiency in donors. STUDY DESIGN AND METHODS: We evaluated the impact of informing donors of their ferritin status on their donation frequency, understanding of iron needs, and actions to improve iron intake 2 years after their inclusion in a prospective observational cohort study. RESULTS: Informing donors of low ferritin results decreased return rate in first-time and repeat donors, and the median number of donations declined from three to two donations/year in returning donors with low ferritin on index donation compared to an increase from 1.5 to 2.5 donations/year in donors with normal ferritin. An electronic questionnaire demonstrated that approximately 60% of low ferritin donors saw their primary medical practitioner, and half of this group started iron. Qualitative interviews revealed suboptimal understanding of iron needs and poor compliance with iron supplementation. CONCLUSION: Providing donors with results of ferritin testing may reduce return rates and donation frequency in the 2 years after testing. Simply providing donors with ferritin results and an information sheet is often inadequate to improve donors' understanding of iron needs and may not lead to a substantive increase in iron intake over time.
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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 | 0.024 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".