Effect of sumac spice, Turkish coffee and yerba mate tea on the postprandial glycemic response to Lebanese mankoucheh
Bibliographic record
Abstract
Purpose – The aim of this study was to evaluate the individual effect of sumac (S), Turkish coffee (C) and yerba mate tea (Y) on the postprandial glycemic response to Lebanese mankoucheh, a common breakfast item in the Lebanese culture, and to determine the glycemic index (GI) of this food. S, C and Y are typical constituents of Lebanese meals. They may influence the postprandial glycemic response to carbohydrate-rich foods, but this has not been studied to date. Design/methodology/approach – Twelve healthy normoglycemic adults consumed on separate days the following test meals: mankoucheh without S (M) with water (control meal); M prepared with single or double doses of S (S1 and S2) with water; M with 60 or 120 mL of unsweetened C; or M with 100 or 200 mL of unsweetened Y. Meals were prepared according to standardized recipes containing 50 g of available carbohydrates. Capillary blood glucose measures were taken at fast and six times after meal ingestion over a two hour period. The GI of mankoucheh was determined using a standard protocol. Findings – The glycemic responses, evaluated at each time following meal ingestion, did not differ significantly among the seven meals, and neither did the incremental area under the glycemic response curves. The GI of mankoucheh was 61 ± 6, with no significant difference between M, M with S1 and M with S2. Originality/value – This study contributes to better characterize the glycemic properties of S, C, Y and mankoucheh in conditions that closely resemble how these dietary items are used and consumed by some cultural groups.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".