Assessing the relative validity of a new, web-based, self-administered 24 h dietary recall in a French-Canadian population
Bibliographic record
Abstract
OBJECTIVE: To assess the relative validity of a new, web-based, self-administered 24 h dietary recall, the R24W, for assessment of energy and nutrient intakes among French Canadians. DESIGN: Each participant completed a 3d food record (FR) and the R24W on three occasions over a 4-week period. Intakes of energy and of twenty-four selected nutrients assessed by both methods were compared. SETTING: Québec City metropolitan area. SUBJECTS: Fifty-seven women and fifty men (mean (sd) age: 47·2 (13·3) years). RESULTS: Equivalent proportions of under-reporters were found with the R24W (15·0%) and the FR (23·4%). Mean (sd) energy intake from the R24W was 7·2% higher than that from the FR (10 857 (3184) kJ/d (2595 (761) kcal/d) v. 10 075 (2971) kJ/d (2408 (710) kcal/d); P<0·01). Significant differences in mean nutrient intakes between the R24W and the FR ranged from -54·8% (i.e. lower value with R24W) for niacin to +40·0% (i.e. higher value with R24W) for alcohol. Sex- and energy-adjusted deattenuated correlations between the two methods were significant for all nutrients except Zn (range: 0·35-0·72; P<0·01). Cross-classification demonstrated that 40·0% of participants were classified in the same quartile with both methods, while 40·0% were classified in the adjacent quartile and only 3·6% were grossly misclassified (1st v. 4th quartile). Analysis of Bland-Altman plots revealed proportional bias between the two assessment methods for 8/24 nutrients. CONCLUSIONS: These data suggest that the R24W presents an acceptable relative validity as compared with the FR for estimating usual dietary intakes in a cohort of French Canadians.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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 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".