Coffee, Tea and Their Additives: Association with BMI and Waist Circumference
Bibliographic record
Abstract
OBJECTIVE: The purposes of this study were to perform a detailed analysis how: i) the frequency of coffee/tea consumption and ii) the use of additives in coffee/tea is associated with measures of total and abdominal obesity. METHOD: 3,823 participants of the 2003-2004 National Health and Nutrition Examination Survey were examined. Obesity was assessed by BMI and waist circumference (WC). Coffee and tea consumption and use of additives were assessed by questionnaire. RESULTS: Coffee consumption was not related to BMI or WC in either gender. However, men who drank ≥2 cups of tea per day had lower BMI (25.9 vs. 28.0 kg/m(2)) and WC (95.2 vs. 101.32 cm) values than men who never drank drink tea (p ≤ 0.05). The associations between tea consumption and BMI or WC were no longer significant after adjustment for additive use. Coffee/tea drinkers who used artificial sweeteners had larger (p ≤ 0.05) BMIs than coffee/tea drinkers who did not use sweeteners (28.2 vs. 27.1 kg/m(2) in men, 28.4 vs. 27.1 kg/m(2) in women). CONCLUSION: Frequency of coffee/tea consumption was not associated with measures of obesity because additive use explained the association between tea consumption and obesity in men. Artificial sweetener use within coffee/tea was associated with higher BMI.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".