Dietary patterns are associated with obesity among Canadian adults (810.14)
Bibliographic record
Abstract
Dietary factors are major contributors to the recent rise in the obesity epidemic. Since foods are consumed in combination, separating the effects of single foods is difficult in observational studies. One possible solution is to derive dietary patterns using data‐reduction techniques. The aim of this study was to identify dietary patterns and their association with obesity among Canadians. Dietary recalls from 11,533 adults in Canadian Community Health Survey (CCHS 2.2) were used. Partial Least Squares analysis was applied to derive dietary patterns using 32 food groups, specifying five obesity‐related nutrients as response variables (fiber, polyunsaturated fat to saturated fat ratio, calcium, cholesterol, fat). Bootstrap method was used to estimate p‐values, confidence intervals (CI), and coefficients of variations. Five dietary patterns were extracted, explaining 61% of response variation: “Western”, “low‐fiber”, “low‐dairy”, “egg” and “low‐processed”. Being in the fourth quartiles of “Western” and “low‐fiber” pattern scores increased the obesity risk by 1.89 (CI:1.4, 2.6) and 1.37 (CI:1.1, 1.7) times respectively, compared to the first quartile (P‐Trend=0.02). Having moderate scores for “egg” and “low‐processed” patterns was associated with 20‐25% reduction in obesity (P‐Trend=0.03). These findings support the benefits of low energy‐dense diets in prevention of obesity in Canadian adults. Grant Funding Source : CCO/CIHR Grant in Population Intervention for Chronic Disease Prevention;Vanier Graduate Scholarship
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".