Adiposity, education and weight loss effort are independently associated with energy reporting quality in the Ontario Food Survey
Bibliographic record
Abstract
OBJECTIVES: To examine the associations of adiposity, dietary restraint and other personal characteristics with energy reporting quality. DESIGN/SUBJECTS: Secondary analysis of 230 women and 158 men from the 1997/98 Ontario Food Survey. METHODS: Energy reporting quality was estimated by ratios of energy intake (EI) to both basal metabolic rate (BMR) and total energy expenditure (TEE). Multivariate regression analyses were conducted to examine energy reporting quality between two dietary recalls and in relation to body mass index (BMI) with adjustment for potential confounders. Energy reporting quality was explored across categories of age, BMI, income, education, dieting status and food insecurity through analysis of variance (ANOVA). RESULTS: From the ANOVA, energy reporting quality was associated with BMI group, age category and weight loss for men and women, as well as with education among women (P 0.05). EI:BMR and EI:TEE on the first and second 24-hour recalls were positively related (P < 0.0001 for men and women). A higher proportion of variance in energy reporting quality was explained for women than for men (R2 = 0.19 and 0.14, respectively). CONCLUSIONS: Studies of diet and adiposity are probably hindered to some extent by BMI-related variation in energy reporting quality. Methods to address this issue are urgently needed if population surveys will continue to serve as the primary source of dietary intake data.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".