Refinement and validation of an FFQ developed to estimate macro- and micronutrient intakes in a south Indian population
Bibliographic record
Abstract
OBJECTIVE: Potential error sources in nutrient estimation with the FFQ include inaccurate or biased recall and overestimation or underestimation of intake due to too many or too few items on the FFQ, respectively. Here we report the refinement of an FFQ that overestimated nutrient intake and its validation against multiple 24 h recalls. STUDY DESIGN: Data on 2527 participants in south India (Trivandrum) were available for the original FFQ (OFFQ) that overestimated nutrient intake (132 food items). After excluding participants with implausible energy intake estimates (<2.72 MJ/d (<650 kcal/d), >15.69 MJ/d (>3750 kcal/d)) we ran stepwise regression analyses with selected nutrients as the outcomes and food intake (servings/d) as predictor variables (n 1867). From these results and expert consultation we refined the FFQ (RFFQ), and validated it by comparing intakes obtained with it and the mean of two 24 h recalls among 100 participants. RESULTS: The OFFQ overestimated usual daily nutrient intake before and after exclusions [for energy: 13.39 (sd 5.46) MJ (3201 (sd 1305) kcal) and 10.96 (sd 2.65) MJ (2619 (sd 634) kcal), respectively]. In stepwise analyses, fifty-seven food items explained 90 % of the variance in nutrients; we retained thirteen food items because participants consumed them at least twice monthly and twelve food items that local nutritionists recommended. Mean energy intake estimated from the RFFQ (eighty-two food items) was 7.94 (sd 2.05) MJ (1897 (sd 489) kcal). The de-attenuated correlations between mean 24 h recall and RFFQ intakes ranged from 0.25 (vitamin A) to 0.82 (fat). CONCLUSION: We refined an FFQ that overestimated nutrient intake by shortening and redesigning, and validated it by comparisons with 24 h dietary recall data.
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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".