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 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".