An updated review of worldwide levels of docosahexaenoic and arachidonic acid in human breast milk by region
Bibliographic record
Abstract
OBJECTIVE: We aimed to evaluate the DHA and arachidonic acid (AA) levels in human breast milk worldwide by country, region and socio-economic status. DESIGN: Descriptive review conducted on English publications reporting breast-milk DHA and AA levels. SETTING: We systematically searched and identified eligible literature in PubMed from January 1980 to July 2015. Data on breast-milk DHA and AA levels from women who had given birth to term infants were included. SUBJECTS: Seventy-eight studies from forty-one countries were included with 4163 breast-milk samples of 3746 individuals. RESULTS: Worldwide mean levels of DHA and AA in breast milk were 0·37 (sd 0·11) % and 0·55 (sd 0·14) % of total fatty acids, respectively. The breast-milk DHA levels from women with accessibility to marine foods were significantly higher than those from women without accessibility (0·35 (sd 0·20) % v. 0·25 (sd 0·14) %, P<0·05). Data from the Asian region showed the highest DHA concentration but much lower AA concentration in breast milk compared with all other regions, independent of accessibility to marine foods. Comparison was made among Canada, Poland and Japan - three typical countries (each with sample size of more than 100 women) from different regions but all with high income and similar accessibility to fish/marine foods. CONCLUSIONS: The current review provides an update on worldwide variation in breast-milk DHA and AA levels and underlines the need for future population- or region-specific investigations.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".