Reproducibility of a food menu to measure energy and macronutrient intakes in a laboratory and under real-life conditions
Bibliographic record
Abstract
Given the limitations associated with the measurement of food intake, we aimed to determine the reliability of a food menu to measure energy intake (EI) and macronutrient intake within the laboratory and under free-living conditions. A total of eight men and eight women (age 25·74 (sd 5·9) years, BMI 23·7 (sd 2·7) kg/m²) completed three identical in-laboratory sessions (ILS) and three out-of-laboratory sessions (OLS). During the ILS, participants had ad libitum access to a variety of foods, which they chose from a menu every hour, for 5 h. For the OLS, the foods were chosen from the menu at the start of the day and packed into containers to bring home. There were no significant differences in total EI (6118·6 (sd 2691·2), 6678·8 (sd 2371·3), 6489·5 (sd 2742·9) kJ; NS) between the three ILS and three OLS (6816·0 (sd 2713·2), 6553·5 (sd 2364·5), 6456·4 (sd 3066·8) kJ; NS). Significant intraclass correlations (ICC) for total energy (r 0·77, P<0·0001), carbohydrate (r 0·81, P<0·0001), dietary fat (r 0·54, P<0·0001) and protein (r 0·81, P<0·0001) intakes for the ILS and significant ICC for total energy (r 0·85, P<0·0001), carbohydrate (0·85, P<0·0001), dietary fat (0·72 P<0·0001) and protein (0·80, P<0·0001) intakes for the OLS were noted. The average within-subject CV for total EI was 18·3 (sd 10·0) and 16·1 (sd 10·3) % for the ILS and OLS, respectively, with a pleasantness rating for foods consumed of 124 (sd 14) mm out of 150 mm (83 %). Overall, the food menu produces a relatively reliable measure of EI inside and outside the laboratory. The results also underscore the difficulties in capturing a representative image of food intake given the relatively high day-to-day variation in the amount and composition of foods consumed.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".