Agreement Between a Food Frequency Questionnaire and the Willett Questionnaire in Overweight or Obese Pregnant Women
Bibliographic record
Abstract
Background & Aims: Overweight and obesity during pregnancy is associated with an increased risk of many adverse health outcomes for both women and their infants. There is a need for simple food frequency questionnaires to assess nutritional intake and aid implementation and evaluation of nutritional interventions in these women. The aim of this study was to compare a newly developed food frequency questionnaire with the Willett food frequency questionnaire in Australian pregnant women who were overweight or obese. Methods: 170 overweight or obese pregnant women (12-20 weeks’ gestation) completed both the Willett and the devised (LIMIT) food frequency questionnaire with n=41 excluded due to unrealistic energy intake or incomplete questionnaires. The mean nutrient intake for each questionnaire and the mean difference in nutrient intake between the questionnaires was assessed. The correlation and agreement between the two questionnaires were assessed by Spearmans correlation coefficients and Bland-Altman method. Results: There were high correlations for total energy intake, protein, carbohydrates, cholesterol, iron, folate, and caffeine (r>0.50, P<0.01), and moderate correlations for fat, fibre, and calcium (r=0.4-0.5, P<0.01). Correlations were low (r<0.3) for vitamins. There was no significant systematic error between the two food frequency questionnaires with the exception of alcohol, calcium, iron, and folate (P>0.05). The limit of agreement (LOA) was wide (LOA <50% or >200%) for macronutrients, calcium, and folate, but within the acceptable range for iron, vitamins, and caffeine (LOA 133%). Conclusions: There is good agreement between the Willett and the LIMIT food frequency questionnaires in estimating macronutrient and some key pregnancy-related micronutrients for group-level comparisons.
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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 | 0.033 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".