The association between frequency of blood donation and the occurrence of low birthweight, preterm delivery, and stillbirth: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Women who donate blood on a regular basis are at high risk of becoming iron depleted. Iron deficiency anemia has been shown to increase the risk of low birthweight and possibly preterm birth. Therefore, there is a concern that regular blood donation by female donors might adversely impact the well-being of their offspring. This retrospective cohort study examined the association between blood donation and the occurrence of adverse pregnancy outcomes. STUDY DESIGN AND METHODS: The study sample included 18,483 female blood donors in their childbearing years (age 18 to 45 years) who delivered during the period 2001 to 2011 in the province of Québec (Canada). The occurrence of low birthweight (<2500 g), preterm delivery (<37 weeks of gestation), and stillbirth was ascertained by linking the donor information with provincial birth and stillbirth registries. RESULTS: There was no association between the frequency of donation in the 2-year period before pregnancy and adverse pregnancy outcomes; compared to women who did not donate during that period, those who donated three or more donations (mean, 3.9 donations) had a relative risk of 0.83 (95% confidence interval [CI], 0.65-1.06) for low birthweight, 0.91 (95% CI, 0.75-1.11) for preterm birth, and 0.62 (95% CI, 0.18-2.12) for stillbirth. These associations remained unchanged after adjusting for baseline characteristics. CONCLUSION: Women who donate blood on a regular but moderate basis do not appear to be at higher risk of adverse pregnancy outcomes. These findings, while reassuring, will need to be replicated in different settings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".