Effect of Daily Iron Supplementation in Healthy Exclusively Breastfed Infants: A Systematic Review with Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The literature on the iron requirements of exclusively breastfed infants contains conflicting data and contrary views. OBJECTIVE: The purpose of this study was to summarize the evidence for both benefits and risks of daily oral iron supplementation with regard to hematologic, growth, cognitive parameters, and adverse effects in exclusively breastfed infants. MATERIALS AND METHODS: Structured electronic searches were conducted using PubMed, Cochrane Library databases, and Google Scholar for randomized controlled trials (RCTs) involving daily iron supplementation in full-term healthy exclusively breastfed infants. Random- and fixed-effects models were used for calculating the pooled estimates. RESULTS: Four RCTs with 511 infants were included in the meta-analysis. Iron interventions had no significant effect on iron deficiency or iron deficiency anemia, serum ferritin level, or hemoglobin level. Iron interventions did result in a significant increase in Bayley psychomotor developmental indices in later life (mean difference [MD] = 7.00, confidence interval [95% CI] 0.99-13.01) and mean corpuscular volume (MD = 2.17 fL; 95% CI 0.99-3.35 fL). Iron supplementation was associated with slower growth during the exclusive breastfeeding period, but the long-term effect is unclear. CONCLUSIONS: Limited available evidence suggests that daily iron supplementation has beneficial effects on hematologic parameters and cognitive development, but may delay physical growth in healthy exclusively breastfed infants. There was no evidence to suggest that iron supplementation could cause other adverse effects.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.028 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".