Maternal High-Dose DHA Supplementation and Neurodevelopment at 18–22 Months of Preterm Children
Bibliographic record
Abstract
OBJECTIVES: To determine whether maternal supplementation with high-dose docosahexaenoic acid (DHA) in breastfed, very preterm neonates improves neurodevelopmental outcomes at 18 to 22 months' corrected age (CA). METHODS: Planned follow-up of a randomized, double-blind, placebo-controlled, multicenter trial to compare neurodevelopmental outcomes in breastfed, preterm neonates born before 29 weeks' gestational age (GA). Lactating mothers were randomized to receive either DHA-rich algae oil or a placebo within 72 hours of delivery until 36 weeks' postmenstrual age. Neurodevelopmental outcomes were assessed with the Bayley Scales of Infant and Toddler Development third edition (Bayley-III) at 18 to 22 months' CA. Planned subgroup analyses were conducted for GA (<27 vs ≥27 weeks' gestation) and sex. RESULTS: Among the 528 children enrolled, 457 (86.6%) had outcomes available at 18 to 22 months' CA (DHA, N = 234, placebo, N = 223). The mean differences in Bayley-III between children in the DHA and placebo groups were -0.07 (95% confidence interval [CI] -3.23 to 3.10, P = .97) for cognitive score, 2.36 (95% CI -1.14 to 5.87, P = .19) for language score, and 1.10 (95% CI -2.01 to 4.20, P = .49) for motor score. The association between treatment and the Bayley-III language score was modified by GA at birth (interaction P = .07). Neonates born <27 weeks' gestation exposed to DHA performed better on the Bayley-III language score, compared with the placebo group (mean difference 5.06, 95% CI 0.08-10.03, P = .05). There was no interaction between treatment group and sex. CONCLUSIONS: Maternal DHA supplementation did not improve neurodevelopmental outcomes at 18 to 22 months' CA in breastfed, preterm neonates, but subgroup analyses suggested a potential benefit for language in preterm neonates born before 27 weeks' GA.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| 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".