Dietary fatty acid determinants of human milk fatty acids.
Bibliographic record
Abstract
Human milk provides fatty acids (FA) for energy and essential n‐6 and n‐3 FA to support infant growth and development. The n‐3 FA docosahexaenoic acid (DHA) particularly important because of its crucial role in neural development and function. Low milk DHA is associated with poor infant visual and neural development. Using a longitudinal blinded intervention with 400mg/d DHA or placebo in gestation, we addressed the relationship between maternal n‐6 and n‐3 fatty acid status in gestation and DHA secretion in milk, and dietary determinants fatty acid intake and milk fatty acids. Blood was collected at 16 and 36 wks gestation, then breast milk collected at 1 mth postpartum for 156 mothers who exclusively breast fed term infants. FA was determined using 100m capillary column GLC. Fatty acid methodology, recovering trans and medium chain FA, impacts the number and accuracy of FA quantization, and data interpretation. Milk DHA was positively correlated to maternal DHA in gestation, varied from 0.06 to 1.25% milk FA, and was inversely related to milk trans FA. Medium chain FA from mammary gland synthesis, varied 3 fold, and were inversely related to milk 18:2n‐6 and 18:3n‐3, but not DHA, 20:5n‐3 or 20:4n‐6. Milk FA vary widely; diets high in hydrogenated fats are associated with low milk DHA, while diets rich in unsaturated vegetable oils may lower MCFA secretion in human milk. 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 | 0.001 | 0.001 |
| 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) | 0.004 | 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".