Women who take n-3 long-chain polyunsaturated fatty acid supplements during pregnancy and lactation meet the recommended intake
Bibliographic record
Abstract
The aim of the current study was to estimate total intake and dietary sources of eicosapentaenoic acid (EPA), docosapentanoic (DPA), and docosahexaenoic acid (DHA) and compare DHA intakes with the recommended intakes in a cohort of pregnant and lactating women. Twenty-four-hour dietary recalls and supplement intake questionnaires were collected from 600 women in the Alberta Pregnancy Outcomes and Nutrition (APrON) cohort at each trimester of pregnancy and 3 months postpartum. Dietary intake was estimated in 2 ways: by using a commercial software program and by using a database created for APrON. Only 27% of women during pregnancy and 25% at 3 months postpartum met the current European Union (EU) consensus recommendation for DHA. Seafood, fish, and seaweed products contributed to 79% of overall n-3 long-chain polyunsaturated fatty acids intake from foods, with the majority from salmon. The estimated intake of DHA and EPA was similar between databases, but the estimated DPA intake was 20%-30% higher using the comprehensive database built for this study. Women who took a supplement containing DHA were 10.6 and 11.1 times more likely to meet the current EU consensus recommendation for pregnancy (95% confidence interval (CI): 6.952-16.07; P<0.001) and postpartum (95% CI: 6.803-18.14; P<0.001), respectively. Our results suggest that the majority of women in the cohort were not meeting the EU recommendation for DHA during pregnancy and lactation, but taking a supplement significantly improved the likelihood that they would meet recommendations.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".