Benefits of Low Glycemic and High Satiety Index Foods for Obesity and Diabetes Control and Management
Bibliographic record
Abstract
Cardiovascular disease risk may be reduced by consuming a low glycemic index diet. Studies have shown clients can successfully incorporate the glycemic index in their dietary routine with positive outcomes. Weight loss may be another benefit found with choosing low glycemic index foods. Diets with high glycemic impact have been postulated to increase risk of obesity, insulin resistance, diabetes and cardiovascular disease. A reduction in the glycemic impact of the diet has been proposed as a means of assisting body weight management, improving blood glucose control and reducing diabetes, cardiovascular and related risks. Safe choices for weight-loss regimens include energy restricted diets calculated according to the Therapeutic Lifestyle Change Diet recommended by the National Cholesterol Education Program, the diet recommended by the Heart Association. There is accumulating evidence that diets containing a lower level of fat and carbohydrates that elicit low glycemic responses (low GI foods or diets) cause important health benefits such as lowering total cholesterol and improving the metabolic control of diabetes. Long-term multicentre randomized intervention trials are needed to improve knowledge on these issues and to determine the contribution of diet, exercise, and metabolic and psychosocial factors to weight loss and weight-loss maintenance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.059 | 0.018 |
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".