The association between food patterns and adiposity in Canadian children
Bibliographic record
Abstract
To describe food patterns associated with obesity, children (n=561) with at least one obese parent, were recruited into the Quebec Adiposity and Lifestyle Investigation in Youth (QUALITY) cohort and studied cross‐sectionally at baseline. Measures of adiposity (BMI, waist circumference, fat mass), screen time, physical activity (accelerometer over 7 d), and diet (three 24‐h food recalls) were collected. Factor analysis was used to identify food patterns. Three food patterns were retained: fast food (sugar‐sweetened beverages, fried potatoes, fried chicken, hamburgers/hot dogs/pizza, salty snacks), traditional food (red meats, poultry, fish, vegetables, etc) and healthy food (whole grains, legumes/nuts/seeds, salads). Higher scores on the fast food pattern were associated with overweight/obesity (BMI≥85th percentile), waist circumference, fat mass percentage after adjustment for age, sex, physical activity, screen time, sleep time, family income, mother's obesity and energy intake (P<0.05). The results provide further evidence of a link between fast food intake and obesity in children. Grant Funding Source : Canadian Institutes of Health Research, Heart and Stroke Foundation of Canada and Fonds de la recherche en santé du Québec
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".