Validation of an automated self-administered 24-hour dietary recall web application against urinary recovery biomarkers in a sample of French-speaking adults of the province of Québec, Canada
Bibliographic record
Abstract
The objective of this study was to validate an automated self-administered 24-hour dietary recall web application (R24W) against recovery biomarkers for sodium, potassium and protein intakes and to identify individual characteristics associated with misreporting in a sample of 61 men and 69 women aged 20–65 years from Québec City, Canada. Each participant completed 3 dietary recalls using the R24W, provided two 24-hour urinary samples and completed questionnaires to document psychosocial factors. Mean reported intakes were 2.2%, 2.1% and 5.0% lower than the urinary reference values, respectively, for sodium, potassium and proteins (significant difference for proteins only (p = 0.04)). Deattenuated correlations between the self-reported intake and biomarkers were significant for sodium (r = 0.48), potassium (r = 0.56) and proteins (r = 0.68). Cross-classification showed that 39.7% (sodium), 42.9% (potassium) and 42.1% (proteins) of participants were ranked into the same quartile with both methods and only 4.8% (sodium), 3.2% (potassium) and 0.8% (proteins) were ranked in opposite quartiles. Lower body esteem related to appearance was associated with sodium underreporting in women (r = 0.33, p = 0.006). No other individual factor was found to be associated with misreporting. These results suggest that the R24W has a good validity for the assessment of sodium, potassium and protein intakes in a sample of French-speaking adults. Novelty: The validity of an automated self-administered 24-hour dietary recall web application named the R24W was tested using urinary biomarkers. According to 7 criteria, the R24W was found to have a good validity to assess self-reported intakes of sodium, potassium and proteins.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".