The effect of a controlled manipulation of maternal dietary fat intake on medium and long chain fatty acids in human breast milk in Saskatoon, Canada
Bibliographic record
Abstract
BACKGROUND: Few studies in recent years have demonstrated the effect of maternal diet on fatty acid composition of human milk. METHODS: Fourteen free-living lactating women participated in a cross-over dietary intervention study, consuming a low fat diet (17.6% of energy as fat, 14.4% of energy as protein, 68.0% of energy as carbohydrate) and a high fat diet (40.3% of energy as fat, 14.4% of energy as protein, 45.3% of energy as carbohydrate) each for periods of 4 days, in randomised order. Each mother was her own control. Mature milk samples were collected during each period and analysed for medium and long chain fatty acids. RESULTS: The concentration of medium chain fatty acids (MCFA), was 13.6% in breast milk for the low fat diet compared to 11.4% for the high fat (p < 0.05). Arachidonic acid (C20:4n-6) levels were significantly higher in breast milk when women consumed the low fat diet. Increased dietary intake of stearic acid (C18:0) and alpha-linolenic acid (C18:3n-3) on the high fat diet significantly increased proportions of these fatty acids in breast milk (p < 0.05) in 4 days. CONCLUSIONS: Changing maternal dietary fat intake has a rapid response in terms of changes to fatty acids in breast milk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".