Fruit and Vegetable Consumption and Body Mass Index: A Quantile Regression Approach
Bibliographic record
Abstract
Background: Empirical evidence on the relationship between consumption of fruits and vegetables and body weight is inconclusive. Previous studies mostly use linear regression methods to study the correlates of the conditional mean of body mass index (BMI). This approach may be less informative if the association between fruit and vegetable consumption and BMI significantly varies across the BMI distribution. Objective: The association between fruit and vegetable consumption and the BMI is examined using quantile regression. Methods: A nationally representative sample of 11,818 individuals from the Canadian Community Health Survey (2004) is used. A quantile regression model is estimated to account for the potential heterogeneous association between fruit and vegetable intake and BMI at different points of the conditional BMI distribution. The analyses are stratified by gender. Results: The multivariate analyses reveal that the association between fruit and vegetable intake and BMI is negative and statistically significant for both males and females; however, this association varies across the conditional quantiles of the BMI distribution. In particular, the estimates are larger for individuals at the higher quantiles of the distribution. The ordinary least squares (OLS) model overstates (understates) the association between FV intake and BMI at the lower (higher) half of the conditional BMI distribution. Conclusion: Findings of the standard models that assume uniform response across different quantiles of BMI distribution may be misleading. The findings of this paper suggest that increasing the intake of fruits and vegetables may be an effective dietary strategy to
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".