Maternal n‐3 docosahexaenoic acid in gestation is associated with better early language development in term gestation infants.
Bibliographic record
Abstract
Docosahexaenoic acid (DHA) is an n‐3 fatty acid present in high amounts in structural lipids of the brain and retina where it plays key roles in neurogenesis and neurite outgrowth, and the visual transduction pathway. Inadequate dietary n‐3 fatty acids during development may have short and long‐term implications for brain development, but whether n‐3 fatty acid deficiency occurs among pregnant women sufficient to limit DHA transfer for optimum fetal brain development is unclear. We used a longitudinal intervention with 400mg/d DHA or placebo from 16 wk gestation until delivery, n=220, and assessed language development at 9 mths of age. DHA supplementation increased the maternal mean erythrocyte phosphatidylethanolamine DHA by 30% at 36 wk gestation. The ability to detect contrasting consonants not present in the native language(s) is present in young infants but lost with development, usually about 9 mth age. We assessed language development using infant‐controlled habituation procedures, as the ability to discriminate phonetic difference in a voiced English and Hindi contrast. More infants of mothers in the DHA supplement than placebo group were unable to distinguish the contrast, consistent with better language development in the supplement group. DHA status in pregnancy is to be important for early infant neural development, yet in some women poor DHA status may be limit infant development. Supported by CIHR.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".