Breast‐fed infants achieve a higher rate of brain and whole body docosahexaenoate accumulation than formula‐fed infants not consuming dietary docosahexaenoate
Bibliographic record
Abstract
Docosahexaenoate (DHA) has been increasingly recognized as an important fatty acid for neural and visual development during the first 6 mon of life. One important point of controversy that remains is the degree to which adequate levels of DHA can be acquired from endogenous synthesis in infants vs. what should be provided as dietary DHA. We have approached this problem by a retrospective analysis of published body composition data to estimate the actual accumulation of DHA in the human infant brain, liver, adipose tissue, remaining lean tissue, and whole body. Estimating whether infants can synthesize sufficient DHA required comparison to and extrapolation from animal data. Over the first 6 mon of life, DHA accumulates at about 10 mg/d in the whole body of breast-fed infants, with 48% of that amount appearing in the brain. To achieve that rate of accumulation, breast-fed infants need to consume a minimum of 20 mg DHA/d. Virtually all breast milk provides a DHA intake of at least 60 mg/d. Despite a store of about 1,050 mg of DHA in body fat at term birth and an intake of about 390 mg/d alpha-linolenate (alpha-LnA), the brain of formula-fed infants not consuming DHA accumulates half the DHA of the brain of breast-fed infants while the rest of the body actually loses DHA over the first 6 mon of life. No experimental data indicate that formula-fed infants not consuming DHA are able to convert the necessary 5.2% of alpha-LnA intake to DHA to match the DHA accumulation of breast-fed infants. We conclude that dietary DHA should likely be provided during at least the first 6 mon of life.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".