Current Information and Asian Perspectives on Long-Chain Polyunsaturated Fatty Acids in Pregnancy, Lactation, and Infancy: Systematic Review and Practice Recommendations from an Early Nutrition Academy Workshop
Bibliographic record
Abstract
The Early Nutrition Academy supported a systematic review of human studies on the roles of pre- and postnatal long-chain polyunsaturated fatty acids (LC-PUFA) published from 2008 to 2013 and an expert workshop that reviewed the information and developed recommendations, considering particularly Asian populations. An increased supply of n-3 LC-PUFA during pregnancy reduces the risk of preterm birth before 34 weeks of gestation. Pregnant women should achieve an additional supply ≥200 mg docosahexaenic acid (DHA)/day, usually achieving a total intake ≥300 mg DHA/day. Higher intakes (600-800 mg DHA/day) may provide greater protection against early preterm birth. Some studies indicate beneficial effects of pre- and postnatal DHA supply on child neurodevelopment and allergy risk. Breast-feeding is the best choice for infants. Breast-feeding women should get ≥200 mg DHA/day to achieve a human milk DHA content of ∼0.3% fatty acids. Infant formula for term infants should contain DHA and arachidonic acid (AA) to provide 100 mg DHA/day and 140 mg AA/day. A supply of 100 mg DHA/day should continue during the second half of infancy. We do not provide quantitative advice on AA levels in follow-on formula fed after the introduction of complimentary feeding due to a lack of sufficient data and considerable variation in the AA amounts provided by complimentary foods. Reasonable intakes for very-low-birth weight infants are 18-60 mg/kg/day DHA and 18-45 mg/kg/day AA, while higher intakes (55-60 mg/kg/day DHA, ∼1% fatty acids; 35-45 mg/kg/day AA, ∼0.6-0.75%) appear preferable. Research on the requirements and effects of LC-PUFA during pregnancy, lactation, and early childhood should continue. © 2014 S. Karger AG, Basel.
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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 | 0.052 | 0.171 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.023 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".