Relevance of haemoglobin monitoring in apheresis plasma donors: A retrospective cohort study in Québec, Canada
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Systematically measuring pre-donation haemoglobin (Hb) levels might be overly cautious for apheresis plasma donation, since plasmapheresis entails a small loss of red blood cells. We explored the association between the frequency of apheresis plasma donation and capillary blood Hb levels. MATERIALS AND METHODS: This retrospective cohort study included donors who gave apheresis plasma at least twice between 24 October 2020 and 23 October 2022 in Québec, Canada. Results were stratified by sex and analysed with linear repeated-measure mixed models with random intercepts. RESULTS: In total, 9535 men (mean age = 46.7 years) and 9409 women (mean age = 41.1 years) made ≥2, but no more than 16 apheresis plasma donations. Over an average of 9.2 months of observation, men maintained Hb levels well above the Hb deferral threshold, and their Hb levels decreased by only 0.17 g/dL between the 1st and 15th donation return (p < 0.0001). Over an average of 9.0 months of observation, women also maintained adequate Hb levels, and their Hb levels decreased by 0.08 g/dL between the 1st and 15th donation return. CONCLUSION: The frequency of apheresis plasma donation was not associated with clinically meaningful changes in Hb levels, neither in men nor in women. This evidence questions the relevance of systematically monitoring Hb for apheresis plasma donation, at least for donation frequencies of ≤7-8 times per year. However, an adverse impact of plasmapheresis on Hb levels cannot be ruled out for individuals donating more frequently or for longer than ~9 months.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".