Modifications in food-group consumption are related to long-term body-weight changes
Bibliographic record
Abstract
Background: Dietary patterns play an important role in the control of body weight.\nObjective: The aim of this study was to verify whether changes in some dietary patterns over a 6-y follow-up period would be associated with weight changes.\nDesign: A sample of 248 volunteers of the Québec Family Study were measured twice (visit 1: 1989–1994; visit 2: 1995–2000). Body weight, percentage body fat, subcutaneous skinfold thicknesses, and waist circumference measurements as well as 3-d dietary and physical activity records were obtained at each visit. At visit 2, all participants filled out a food-based questionnaire examining changes in the consumption of 10 food categories. To further investigate the relation between changes in food-group consumption and bodyweight changes, a total of 51 food subcategories were identified from dietary records.\nResults: A self-reported decrease in the consumption of food in the fat group or an increase in consumption in the fruit group from the food-based questionnaire predicted a lower increase in body weight and adiposity indicators over time. A more detailed examination of the change in food groups between diet records revealed that increases in the consumption of whole fruit as well as skimmed milk and partly skimmed milk were the 2 food patterns that negatively correlated with the changes of each body weight–related indicator. \nConclusions:These results show that changes in the consumption of some specific food groups are associated with body-weight changes. Such specific eating patterns could help to improve obesity treatment and prevention.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".