Development, reproducibility and validity of the food frequency questionnaire in the Poland arm of the Prospective Urban and Rural Epidemiological (PURE) study
Bibliographic record
Abstract
BACKGROUND: A food frequency questionnaire (FFQ) is the most commonly used method in large epidemiological studies. The validation of an FFQ is essential for specific populations because foods are culture-dependent. The present study aimed to develop an FFQ and evaluate its validity and reproducibility in estimating the intake of nutrients in urban and rural areas of Poland. METHODS: Adult participants (n = 146) in the Polish arm of the ongoing Prospective Urban and Rural Epidemiological (PURE) study completed FFQs on two occasions, as well as four 24-h dietary recalls (DRs) during a 12-month period. Correlation coefficients (r) and de-attenuated correlation coefficients between dietary recalls and both FFQs were calculated for selected macro- and micronutrients. Agreement between the two methods was evaluated by classification into quartiles and the Bland-Altman method. Reproducibility was assessed by the intra-class correlation coefficient (ICC). RESULTS: The final food list contained 134 food items. For urban participants, FFQ2 generally underestimated energy, protein and fat compared to the FFQ1 and mean of DRs. In rural areas, compared to DRs, both FFQs overestimated energy and macronutrients. For both urban and rural settings, de-attenuated correlation exceeded 0.4 for almost all nutrients and the exact agreement in quartile categorisation was >66%. When assessing repeatability, ICC varied from 0.39-0.63 in an urban setting and 0.19-0.45 in a rural setting. CONCLUSIONS: This 134-item FFQ has good validity and reproducibility in relation to the reference method and can be used to rank individuals based on their macro- and micronutrient intake.
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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 | 0.029 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".