Validation of a Food Frequency Questionnaire for Bone Nutrients in Pregnant Women
Bibliographic record
Abstract
PURPOSE: The aim was to validate a food frequency questionnaire (FFQ) against a 3-day food record (3DFR) for pregnant women with a focus on nutrients important for bone health from food and supplements. METHODS: The FFQ and 3DFR were administered to pregnant women (n = 42) aged 18-45 years in their third trimester of pregnancy in Hamilton, Ontario. Nutrient analysis of intakes was conducted using an FFQ calculator and Nutritionist-Pro software. The average daily serving consumption of Milk and alternatives group and Vegetable subgroup from Canada's Food Guide were also compared. RESULTS: There was a high positive correlation between methods for total dietary vitamin D (r = 0.83). Low positive associations were observed for total protein (r = 0.37), calcium (r = 0.36), vitamin K (r = 0.41), and servings of Milk and alternatives (r = 0.36). A cross-classification analysis using participants' intake quartiles revealed no major misclassifications. Bland-Altman analysis showed that the FFQ mildly underestimated the intake for protein, whereas it grossly overestimated the intake of vitamin K, and daily servings of Milk and alternatives and Vegetable. CONCLUSIONS: This FFQ can serve as a useful tool in clinical and research settings to assess key bone nutrients from foods and supplement sources in pregnant women.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".