Underreporting of energy intake from a self-administered food-frequency questionnaire completed by adults in Montreal
Bibliographic record
Abstract
BACKGROUND: Energy intake determined from self-reported dietary assessment methods may be underreported. Therefore, it is important that such methods be validated against another with known validity for energy intake or energy expenditure. METHODS: We investigated potential underestimation of energy intake obtained from our semi-quantitative food-frequency questionnaire (FFQ) administered between 2000 and 2001 in the metropolitan area of Montreal, Canada. The study population included 246 adults aged 18 to 82 years. The ratio of energy intake to estimated basal metabolic rate (EI/BMR) was used to assess underreporting and physical activity was determined from self-administered questions. Comparison of the EI/BMR ratio with the Goldberg statistical cut-off allowed us to detect individuals who were low energy reporters (LERs). LERs and non-LERs were compared to determine if they differed on sociodemographic, anthropometric and lifestyle variables. RESULTS: The EI/BMR ratio was 1.26 for men and 1.32 for women. LERs represented 43% of the sample of individuals. Male LERs accounted for 54% compared with 35% among females. Underreporting of energy intake was highest in men and individuals who were older, heavier, with higher body mass index and lower education level. A higher proportion of male LERs perceived their financial situation as adequate while a greater proportion of female LERs considered themselves poor. CONCLUSION: Our data suggest that underreporting of energy intake from the FFQ was considerable and may bias dietary interpretation. As this was uneven across the sample, it is crucial to recognise the characteristics of LERs in order to increase the validity of reported energy intake.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".