Coffee, Tea and Their Additives: Association with BMI and Waist Circumference
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 |
| 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 it