Examining the Advantages of Using Multiple Web-Based Dietary Assessment Instruments to Measure Population Dietary Intake: The PREDISE Study
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Bibliographic record
Abstract
BACKGROUND: Combining traditional dietary assessment instruments has been suggested to improve precision of dietary intake estimates. However, this has not been investigated using web-based 24-h recall (R24W) or a web-based food-frequency questionnaire (wFFQ). OBJECTIVE: The aim of this study was to compare different combinations of web-based instruments to assess population-level dietary intake estimates (means and percentiles) and their precision, either with or without statistical modeling of within-person day-to-day variations. METHODS: As part of the cross-sectional PREDISE study, 1025 French-speaking adults completed 3 randomly allocated R24W and 1 wFFQ within 21 d. Crude estimates of intake were generated from either 1 or 3 repeated R24W. The National Cancer Institute (NCI) method was used to account for within-person variation. Usual intakes were modeled from 1 R24W repeated in a subsample (40%) and from 3 R24W, with or without consideration of data from the wFFQ. RESULTS: Using crude data from 3 R24W increased precision of estimates and modified distribution of intakes compared with using data from only 1 R24W. Using NCI-modeled data from 3 repeated R24W had no impact on the precision around mean intakes but increased precision of low and high percentiles intake estimates compared with NCI-modeled data from a partially repeated R24W. Considering data from a wFFQ in combination with data derived from 3 R24W did not influence the precision of intake estimates of most foods and nutrients. CONCLUSIONS: The data suggest that relying on repeated measures of food and nutrient intake through R24W is preferable to single assessment when within-person variation is not considered. Data also suggest that when NCI modeling is applied, using 3 R24W only improves the precision of low and high percentiles intake estimates compared with using a partially repeated web-based recall.
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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 | 0.001 |
| 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) | 0.000 | 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 it