Docosahexaenoic acid-rich algae oil supplementation on breast milk fatty acid profile of mothers who delivered prematurely: a randomized clinical trial
Bibliographic record
Abstract
Preterm infants are deficient in long-chain polyunsaturated fatty acids, especially docosahexaenoic acid (DHA), a fatty acid (FA) associated with an increase in bronchopulmonary dysplasia (BPD). In two previous randomized control trials, DHA supplementation did not reduce the risk of BPD. We examined the breast milk FA profile, collected 14 days after birth, of mothers who delivered before 29 weeks of gestation and who were supplemented with DHA-rich algae oil or a placebo within 72 h after birth as part of the MOBYDIck trial. Milk FA were analyzed by gas chromatography. The total amount of FA (mg/mL) was similar in both groups but the supplementation increased DHA (expressed as % of total FA, mean ± SD, treatment vs placebo, 0.95 ± 0.44% vs 0.34 ± 0.20%; P < 0.0001), n-6 docosapentaenoic acid (DPA) (0.275 ± 0.14% vs 0.04 ± 0.04%; P < 0.0001) and eicosapentaenoic acid (0.08 ± 0.08% vs 0.07 ± 0.07%; P < 0.0001) while decreasing n-3 DPA (0.16 ± 0.05% vs 0.17 ± 0.06%; P < 0.05). Supplementation changed the ratio of DHA to arachidonic acid (1.76 ± 1.55% vs 0.60 ± 0.31%; P < 0.0001) and n-6 to n-3 FA (0.21 ± 0.06% vs 0.17 ± 0.04%; P < 0.0001). DHA-rich algae supplementation successfully increased the DHA content of breast milk but also included secondary changes that are closely involved with inflammation and may contribute to changing clinical outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".