Glycemic Index and Glycemic Load of Selected Omani Rice Dishes
Bibliographic record
Abstract
Rice (Oryza sativa) is one of the most important cereal grains that is popularly consumed globally as a staple food. A number of rice dishes are prepared and consumed in Oman. Representative samples of four different types of cooked rice dishes (White rice, Brown rice, Kabuli rice, and Biryani rice) and two traditional dishes (Arsiya and Harees) were collected from local restaurants. The aim of this study was to evaluate the proximate composition, glycemic index (GI) and glycemic load (GL) of these dishes. The results indicated significant (P<0.05) differences in the proximate composition, GI, and GL values of differently cooked rice dishes as well as for Arsiya and Harees. With the exception of white rice which showed the highest glycemic index value (77.3), all other three rice dishes as well as Arsiya and Harees were within the medium GI category (59.5 to 62.9). Similarly, the white rice showed the highest glycemic load value (20.9), whereas the other three rice dishes were within the medium GL category (10.7 to 16.7). The Arsiya and Harees were within low GL category (5.4 to 6.2). The method of cooking appears to affect the proximate composition, starch gelatinization, release of glucose and glycemic index of these dishes. We are reporting for the first time the GI and GL values of these Omani dishes. The results will help in developing appropriate dietary management strategies in meal planning by using the concept of GI and GL for both the normal and diabetic subjects to reduce their risk of chronic diseases.
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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 | 0.001 | 0.001 |
| 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".