Effect of Coffee/Tea on Mean Values and Variability of The Glycemic Index of Foods
Bibliographic record
Abstract
There is accumulating evidence that a low glycemic index (GI) diet may reduce the risk of a number of chronic diseases, thus, there is increasing interest in GI in nutrition research. Since numerous methodological factors influence GI determination, valid use of the concept requires accurate and precise methodology. One factor that has been assumed to be important is the type of drink served with test foods. Thus, to see if allowing subjects to drink coffee/tea affected the mean and variation of GI, the GI values of Fruit Leather (FL), and Cheese Puffs (CP) were determined twice in 10 subjects using the FAO/WHO protocol with white bread (WB) as the reference food. In one series subjects could choose either 250 ml coffee or tea with the test foods, while in the other they had 250 ml water as the drink. Coffee/tea increased blood glucose (BG) 30 min after the WB and CP and reduced BG at 120 min compared to water (p<0.05). Similar trends were seen for FL (p>0.05). There were no significant differences between drinks on mean iAUC and mean GI values for all foods (p>0.05). The within‐subject coefficient of variation of iAUC (CV=100 × SD/mean) for the repeated WB tests with coffee/tea, 21±3.0 %, was less than with water, 30±5.3 %, although the difference was not significant. The GI SEM for FL and CP with coffee/tea 5.6 and 8.3, were less than those with water 49.3 and 11.6. These results suggest that coffee/tea do not affect the mean but may improve the precision of GI values.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".