Pre‐pregnancy micronutrient intake assessed by food frequency questionnaire in the Alberta Pregnancy Outcomes and Nutrition (APrON) Study
Bibliographic record
Abstract
Nutrient intake prior to pregnancy may impact maternal health and fetal development. A food frequency questionnaire (FFQ) was used to retrospectively assess diet and supplement intake over the year prior to pregnancy in the first cohort of the APrON study. Fifteen participants were excluded based on extremes in average caloric intake (<600kcal/day, n=3; >3500kcal/day, n=12). The FFQ was successfully completed by 491 women with a mean age of 31 ± 4 years and a mean body mass index of 24.0 ± 4.6 kg/m 2 . Key micronutrients chosen for assessment were iron, calcium and vitamin D. Mean (± Standard Deviation) intakes from food and supplements were 21.5 ± 9.8 mg of iron, 1225.8 ± 565.2mg of calcium, and 11.7 ± 6.4 μg of vitamin D. The proportion of women meeting the recommended dietary allowance (RDA) from food and supplements during this potentially critical period was low for vitamin D (38%) and moderate for iron (64%) and calcium (62%). These data illustrate a low to moderate intake of key micronutrients in Alberta women prior to pregnancy. Assessment and counseling to improve key micronutrient intakes of women in their child‐bearing years may be warranted. Research supported by Alberta Innovates ‐ Health Solutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".