Docosahexaenoic acid level of breast milk of iranian women in low fish – consuming and high fish – consuming regions
Bibliographic record
Abstract
Objective: Sufficient intakes of docosahexaenoic acid (DHA) via breast milk are required for optimizing visual and neural development at early stages of life. Little or no information is available on DHA intakes and levels found in breast milk in Iranian women and surrounding regions. In this study we measure the DHA in the breast milk of lactating Iranian women from low and high – fish-eating regions and estimate their DHA intakes. Methods: This is a cross-sectional and prospective study done in two cities of Iran (Mashhad and Amol); 10 ml of mature breast milk were obtained from 40 healthy lactating women (selected randomly) at Imam Reza Hospital of Mashhad (a low fish-consuming area) and Amir Kola Children Hospital of Amol city (a high fish-consuming area). The data were analyzed in two samples by using t- independent test and Mann-Whitney test via SPSS version 11.5 software. Results: The breast milk DHA levels of mothers living in the high fish-consuming area (Amol) were significantly higher than mothers living in the low fish-consuming area (Mashhad, p< 0.01). It can be estimated that the average DHA intake of lactating Iranian women is approximately 184 mg/dl in Mashhad and 307 mg/dl in Amol. Conclusion: The DHA content of breast milk was higher in high-fish consuming area in comparison to the low-fish consuming area in Iran indicating that the DHA levels of breast milk are influenced by fish consumption.
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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 | 0.001 |
| 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) | 0.001 | 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".