The macronutrient composition of the evening meal before glycemic index testing has no effect on glycemic response
Bibliographic record
Abstract
Background A criticism of glycemic index (GI) is the large variation in glycemic response, both between and within individuals. To reduce this variation it is recommended that GI testing of a food be conducted under standardized conditions. One recommendation is that each subject consumes the same evening meal before each test day. Given the demands placed on participants it is important to assess whether such a restriction is necessary. Aim To determine whether manipulating the fat and carbohydrate content of the evening meal preceding GI testing has an effect on glycemic response. Methods Using a randomized crossover design, 20 participants completed a series of 8 tests. Test foods consisted of 2 glucose beverages and 2 bread samples, containing 50g of available carbohydrate. Each test food was consumed following a high carbohydrate or a high fat meal the night preceding testing. Blood glucose concentration was measured at regular intervals over 2 hours and area under the curve (AUC) determined. Results There were no significant differences in the AUC for the glucose drink [ratio (95% CI); 0.91 (0.80, 1.03)] or bread [1.15; (0.97, 1.36)] with the high carbohydrate and the high fat evening meal. Conclusion The fat and carbohydrate content of the evening meal prior to GI testing does not influence blood glucose response, thus participants should be allowed some flexibility in evening meal selection.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".