Effect of coffee and tea on the glycaemic index of foods: no effect on mean but reduced variability
Bibliographic record
Abstract
Coffee and tea may influence glycaemic responses but it is not clear whether they affect the glycaemic index (GI) value of foods. Therefore, to see if coffee and tea affected the mean and SEM of GI values, the GI of fruit leather (FL) and cheese puffs (CP) were determined twice in ten subjects using the FAO/WHO protocol with white bread as the reference food. In one series subjects chose to drink 250 ml of either coffee or tea with all test meals, while in the other series they drank 250 ml water. The tests for both series were conducted as a single experiment with the order of all tests being randomised. Coffee and tea increased the overall mean peak blood glucose increment compared with water by 0.25 (SEM 0.09) mmol/l (P=0.02), but did not significantly affect the incremental area under the glucose response curve. Mean GI values were not affected by coffee or tea but the SEM was reduced by about 30% (FL: 31 (SEM 4) v. 35 (SEM 7) and CP: 76 (SEM 6) v. 75 (SEM 8) for coffee or tea v. water, respectively). The error mean square term from the ANOVA of the GI values was significantly smaller for coffee or tea v. water (F(18, 18) = 2.31; P=0.04). We conclude that drinking coffee or tea with test meals does not affect the mean GI value obtained, but may reduce variability and, hence, improve precision.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".