Validation of a Quantitative Food Frequency Questionnaire for a Japanese Population in Hawaii
Bibliographic record
Abstract
Abstract. Objective: To measure the validity of a quantitative food frequency questionnaire (QFFQ). Design : A cross-sectional validation study of the QFFQ against a four-day food record (4DR) using Spearman correlation, cross-classification, kappa statistics, and Bland–Altman plotting. Setting : The Gastroenterology Department of Kaiser Permanente Hawaii. Subjects: 76 healthy Japanese American men and women, aged 40–75 years. Results : Somewhat stronger average correlations were observed between the QFFQ and the 4DR for macronutrients compared to micronutrients (Spearman rho of 0.47 vs. 0.35). Moderate correlations between the two tools were observed for macronutrients (including saturated fatty acids and dietary fibre), iron, β-carotene, vitamin C, and ethanol ( rho: 0.38–0.58). Overall, stronger correlations were found among men than women between the two tools (mean rho 0.41 vs. 0.26). In a cross classification analysis, for more than 75% of the observations, a complete to relative agreement between the two methods was observed for fat, α-carotene, folate, vitamin D, and ethanol. Sex difference in agreement was minimal in cross-classification (overall extreme misclassification of 9.80% for men and 12.40% for women). Bland–Altman plots showed over-estimations of dietary fibre and α-carotene intake and an under-estimation of cholesterol intake by the QFFQ at high levels of consumption. However, the QFFQ estimation for fat, dietary fibre, folate, cholesterol, α-carotene, vitamins D and C, and ethanol intake was less than 7% different compared to the 4DR. Conclusions: The QFFQ has an adequate validity for fat, folate, vitamin D, and ethanol and can correctly categorize participants for intakes of cholesterol, dietary fibre, α-carotene, and zinc.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".