Nutritional Management of Diabetes Mellitus: An appraisal of the role of Medicinal plants
Bibliographic record
Abstract
Diabetes mellitus is a heterogeneous metabolic syndrome characterized by chronic hyperglycemia with partial or total lack of insulin secretion and insulin resistance. The most common symptoms are polydipsia, polyuria, blurred vision, slow healing sore, nerve damage. Diabetes, being a metabolic, endocrine disorder is directly connected to carbohydrates, lipid, and protein metabolism. As a result, nutrition therapy forms an integral part of diabetes management. Daily caloric intake of 50% - 55% carbohydrates is recommended. Carbohydrate with low glycaemic index is preferred to those with high glycaemic index. Nonnutritive sweeteners are also encouraged for people with diabetes to add increased variety to their food choices. The protein requirement for persons with diabetes is not different from the general population, 15-20 % of total caloric intake. Both soluble and insoluble fibers are encouraged in amounts similar to the recommendations for the general population (20–35 g). Fibers are useful as they prolong gastric emptying, prevent constipation, lower serum cholesterol level, and reduce nutrients diffusion rates, thus reducing blood glucose response. A low-fat diet is advisable for diabetics in order to reach and maintain good weight and health. As part of a healthy diet, 30% of daily calories should come from fats, and of these less than 10% should be saturated fat, less than 10% polyunsaturated fat and 10- 15% monounsaturated fat. As the general population, people with diabetes have no need for vitamin and mineral supplementation when the dietary intake is adequate. Despite much research on nutritional factors in the etiology and management of diabetes, the risk associated with several individual nutrients is not entirely clear. In order to achieve maximum benefit from nutritional interventions in the management of diabetes changes in government policies and legislation will be needed in addition to individual and community-based programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".