Iron supplementation during pregnancy: what are the risks and benefits of current practices?
Bibliographic record
Abstract
Iron-deficiency anemia is still prevalent among pregnant women living in industrialized countries such as Canada. To prevent this deficiency, iron supplements (30 mg/d) are routinely prescribed to Canadian pregnant women. Recently, dietary reference intakes for iron have increased from 18 and 23 mg/d during the second and third trimesters, respectively, to 27 mg/d throughout the pregnancy for all age groups. Whether this new recommendation implies an increase of iron dosage in supplements has not been answered. Are there any benefits or risks for the mother and her infant associated with iron supplementation during pregnancy? If iron supplementation is recommended, what should be the ideal dosage? This article reviews current knowledge on the potential negative or positive impact of iron supplementation during pregnancy on the outcomes of both infants and mothers. Based on the literature reviewed, a low daily dose of iron (30 mg elemental iron) during pregnancy improves women's iron status and seems to protect their infants from iron-deficiency anemia. Several studies have also shown that a low daily dose of iron may improve birth weight even in non-anemic pregnant women. However, higher dosages are not recommended because of the potential negative effects on mineral absorption, oxidative pathways, and adverse gastrointestinal symptoms. To date, it is still not clear if health professionals should recommend routine or selective supplementation. However, neither routine nor selective iron supplementation during pregnancy is able to eliminate iron-deficiency anemia. Even though the dietary reference intake for iron during pregnancy has been recently increased, we do not recommend higher doses of iron in supplements designed for pregnant women.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".