Longer‐term Effects of a Low Glycemic Index Diet on Glycemic Control in Type 2 Diabetes
Bibliographic record
Abstract
Background Nut consumption, including peanuts, has been associated with a reduced risk of coronary heart disease (CHD). More recently, interest has grown in the potential value of including nuts in diets of individuals with diabetes. Objective To determine if tree nuts and peanuts improve glycemic control in non‐insulin dependent diabetes, as assessed by HbA1c and to assess whether these outcomes relate to improvements in CHD risk (serum lipids, blood pressure and oxidative stress and inflammatory biomarkers). Methods Approximately 120 NIDDM subjects (BMI ≤32kg/m 2 ) treated with oral hypoglycemic agents (HbA1c 6.5‐8.0%) were recruited to a 3 month parallel design study. Subjects were randomized to one of three treatments: 1) Test (Full Dose Nut Diet): Raw nuts were added as supplements to the subject's usual diet based on required energy intake (≥2,400kcal/d received 100g/d nuts, ≈600kcal; 1,600‐2,400kcal/d received 75g/d nuts, ≈450kcal; ≤1,600kcal/d received 50g/d, ≈300kcal); 2) Test (Half Dose Nut Diet): Subjects received half dose of nuts and half dose of control muffin according to calorie needs; and 3) Control: whole wheat muffins were matched with energy content of nut supplements. One‐week weighed diet histories were obtained and fasting blood samples collected at baseline and weeks 2, 4, 8, 10 and 12 for markers of glycemic control and CHD risk factors. Results Final data to be presented.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".