Iron deficiency in <scp>C</scp>anadian blood donors
Bibliographic record
Abstract
BACKGROUND: The adequacy of communication and knowledge of donors and physicians regarding iron needs and the relationship between hemoglobin (Hb) and iron stores require evaluation to address donor iron deficiency. STUDY DESIGN AND METHODS: A prospective cohort study was performed on 550 successful donors and 50 donors deferred for low Hb (<125 g/L on repeat fingerstick). Donors participated in an on-clinic interview and had serum ferritin measured. They were mailed their results and recontacted regarding follow-up. RESULTS: Most donors are unaware of possible health impacts of donation and do not discuss donation with their physician. In successful donors, mean ferritin levels were 37 and 131 μg/L in first-time and reactivated (no donation for 2 years) females and males and 19 and 29 μg/L in frequent repeat females and males, respectively (p < 0.0001), with infrequent donors having intermediate results. Mean ferritin was 12 μg/L in donors deferred for low Hb. Twenty of 22 donors failing initial Hb testing and passing on repeat testing had ferritin below 25 μg/L. On follow-up, 63 of 164 donors (38%) with low ferritin were taking iron supplements 2 months postdonation. CONCLUSION: Iron deficiency is frequent, particularly in female donors and frequent donors. A fail on initial Hb testing followed by a pass on repeat testing is likely to be due to iron deficiency and borderline anemia. Donors and physicians need to be more aware of iron needs associated with blood donation and appropriate treatment for low iron stores.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".