Validation of a Semi-Quantitative Food Frequency Questionnaire for Argentinean Adults
Bibliographic record
Abstract
BACKGROUND: The Food Frequency Questionnaire (FFQ) is the most commonly used method for ranking individuals based on long term food intake in large epidemiological studies. The validation of an FFQ for specific populations is essential as food consumption is culture dependent. The aim of this study was to develop a Semi-quantitative Food Frequency Questionnaire (SFFQ) and evaluate its validity and reproducibility in estimating nutrient intake in urban and rural areas of Argentina. METHODS/PRINCIPAL FINDINGS: Overall, 256 participants in the Argentinean arm of the ongoing Prospective Urban and Rural Epidemiological study (PURE) were enrolled for development and validation of the SFFQ. One hundred individuals participated in the SFFQ development. The other 156 individuals completed the SFFQs on two occasions, four 24-hour Dietary Recalls (24DRs) in urban, and three 24DRs in rural areas during a one-year period. Correlation coefficients (r) and de-attenuated correlation coefficients between 24DRs and SFFQ were calculated for macro and micro-nutrients. The level of agreement between the two methods was evaluated using classification into same and extreme quartiles and the Bland-Altman method. The reproducibility of the SFFQ was assessed by Pearson correlation coefficients and Intra-class Correlation Coefficients (ICC). The SFFQ consists of 96 food items. In both urban and rural settings de-attenuated correlations exceeded 0.4 for most of the nutrients. The classification into the same and adjacent quartiles was more than 70% for urban and 60% for rural settings. The Pearson correlation between two SFFQs varied from 0.30-0.56 and 0.32-0.60 in urban and rural settings, respectively. CONCLUSION: Our results showed that this SFFQ had moderate relative validity and reproducibility for macro and micronutrients in relation to the comparison method and can be used to rank individuals based on habitual nutrient intake.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".