Validation of a self-administered web-based 24-hour dietary recall among pregnant women
Bibliographic record
Abstract
BACKGROUND: The use of valid dietary assessment methods is crucial to analyse adherence to dietary recommendations among pregnant women. This study aims to assess the relative validity of a self-administered Web-based 24-h dietary recall, the R24W, against a pen-paper 3-day food record (FR) among pregnant women. METHODS: Sixty (60) pregnant women recruited at 9.3 ± 0.7 weeks of pregnancy in Quebec City completed, at each trimester, 3 R24W and a 3-day FR. Mean energy and nutrient intakes reported by both tools were compared using paired Student T-Tests. Pearson correlations were used to analyze the association between both methods. Agreement between the two methods was evaluated using cross-classification analyses, weighted kappa coefficients and Bland-Altman analyses. RESULTS: (r = 0.03; p = 0.83) and ranged from 0.27 to 0.76 (p < 0.05). Differences between mean intakes assessed by the R24W and the FR did not exceed 10% in 19 variables and were not significant for 16 out of 26 variables. In cross-classification analyses, the R24W ranked, on average, 79.1% of participants in the same or adjacent quartiles as the FR. CONCLUSIONS: Compared to a 3-day FR, the R24W is a valid method to assess intakes of energy and most nutrients but may be less accurate in the evaluation of intakes of fat (as a proportion of energy intake), vitamin D, zinc and folic acid. During pregnancy, the R24W was a more accurate tool at a group-level than at an individual-level and should, therefore, be used in an epidemiological rather than a clinical setting. The R24W may be particularly valuable as a tool used in cohort studies to provide valid information on pregnant women's dietary intakes and facilitate evaluation of associations between diet and adverse pregnancy outcomes.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".