Adequacy of nutritional intake during pregnancy in relation to prepregnancy BMI: results from the 3D Cohort Study
Bibliographic record
Abstract
Our study compares adequacy of nutritional intakes among pregnant women with different prepregnancy BMI and explores associations between nutritional intakes during pregnancy and both prepregnancy BMI and gestational weight gain (GWG). We collected dietary information from a large cohort of pregnant Canadian women (n 861) using a 3-d food record. We estimated usual dietary intakes of energy (E), macronutrients and micronutrients using the National Cancer Institute method. We also performed Pearson's correlations between nutritional intakes and both prepregnancy BMI and GWG. In all BMI categories, intakes considered suboptimal (by comparison with estimated average requirements) were noted for Fe, vitamin D, folate, vitamin B6, Mg, Zn, Ca and vitamin A. Total fat intakes were above the acceptable macronutrient distribution range (AMDR) for 36 % of the women. A higher proportion of obese women had carbohydrate intakes (as %E) below the AMDR (v. normal-weight and overweight women; 19 v. 9 %) and Na intakes above the tolerable upper intake level (v. other BMI categories; 90 v. 77-78 %). In all BMI categories, median intakes of K and fibre were below adequate intake. Intakes of several nutrients (adjusted for energy) were correlated with BMI. Correlations were detected between energy-adjusted nutrient intakes and total GWG and were, for the most part, specific to certain BMI categories. Overweight and obese pregnant women appear to be the most nutritionally vulnerable. Nutrition interventions are needed to guide pregnant women toward their optimal GWG while also meeting their nutritional requirements.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".