Reproducibility and validity of a food frequency questionnaire for the assessment of vitamin D intake in Canadian lactating women
Bibliographic record
Abstract
Valid dietary assessment tools to capture vitamin D intake from foods are required to facilitate research regarding the relationships among intake and status. To our knowledge, no prior study has validated a food frequency questionnaire (FFQ) for assessment of vitamin D intake in lactating women. The objective of this study was to validate the Willett FFQ, adapted for Canadians, for assessing vitamin D intake in lactating women. Healthy women (n=42) from Montreal completed a FFQ at 4 months postpartum and 24 hour dietary recalls at 1, 2.5, and 4 months postpartum. Fasted venous blood was collected for analysis of plasma 25‐ hydroxyvitamin D (25(OH)D) by LIAISON® (Diasorin, Stillwater, MN). A subsample (n=7) completed the FFQ at baseline for reproducibility testing. Mean vitamin D intake was similar between FFQ and recalls (p>0.05). There was a significant correlation between FFQ and recalls for dietary and total vitamin D (p<0.01) but not between FFQ and 25(OH)D. Bland‐Altman analyses indicated a mean difference of −10 IU/d (LoA: 359, −380 IU) between dietary methods. Based on total vitamin D intake, 69% were classified into the same tertile with KW=0.63 between dietary methods and 45.2% were classified into the same tertile with KW=0.14 between FFQ and 25(OH)D. These findings suggest that the Canadian adapted Willet FFQ may be a valid tool for the assessment of vitamin D intake among lactating women. (Supported by CFDR).
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".