Trimester-Specific Dietary Intakes in a Sample of French-Canadian Pregnant Women in Comparison with National Nutritional Guidelines
Bibliographic record
Abstract
Diet during pregnancy greatly impacts health outcomes. This study aims to measure changes in dietary intakes throughout trimesters and to assess pregnant women’s dietary intakes in comparison with current Canadian nutritional recommendations. Seventy-nine pregnant women were recruited and completed, within each trimester, three Web-based 24-h dietary recalls and one Web questionnaire on supplement use. Dietary intakes from food, with and without supplements, were compared to nutritional recommendations throughout pregnancy. Energy and macronutrient intakes remained stable throughout pregnancy. A majority of women exceeded their energy and protein requirements in the first trimester, and fat intakes as a percentage of energy intakes were above recommendations for more than half of the women in all trimesters. Supplement use increased dietary intakes of most vitamins and minerals, but 20% of women still had inadequate total vitamin D intakes and most women had excessive folic acid intakes. This study showed that pregnant women did not increase their energy intakes throughout pregnancy as recommended. Furthermore, although prenatal supplementation reduces the risk of inadequate intake for most micronutrients, there is still a risk of excessive folic acid and insufficient vitamin D intake, which needs further investigation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".