A large national study of ferritin testing in Canadian blood donors
Bibliographic record
Abstract
BACKGROUND: We assessed risk groups for iron deficiency and the feasibility and efficacy of ferritin testing in a large blood center. STUDY DESIGN AND METHODS: Donors were informed of possible testing in the predonation pamphlet. Plasma ferritin was measured on retention samples (n = 12,595) from representative clinics. Low-ferritin donors (<25 µg/L) were sent a letter and information sheet and not called for 6 months. Ferritin testing was repeated on 25% of donors; donor return rate and frequency were monitored. RESULTS: Low-ferritin donors represented 2.9% of first-time and reactivated (no donation in past 12 months) male donors, 32.2% of first-time and reactivated female donors, 41.6% of repeat male donors, and 65.1% of repeat female donors. A mean of 11.7 months after index donation, the return rate was 76% for normal and 58% for low-ferritin donors; returning low-ferritin donors had made approximately one fewer donation. Ferritin increased by 16.3 and 12.1 µg/L in male and female low-ferritin donors and decreased by 17 µg/L in male and female normal-ferritin donors. CONCLUSION: The minimum hemoglobin level will be increased to 130 g/L for male donors and the minimum interdonation interval changed to 84 days (four donations yearly) for female donors based on iron deficiency risk groups. Large-scale ferritin testing was feasible. Donors informed of low-ferritin results had a lower return rate, donated less often, and had an increase in ferritin on return, approximately 1 year after initial testing, compared to donors with normal ferritin results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".