Effect of Nuts on Coronary Heart Disease Risk Factors in Type 2 Diabetes
Bibliographic record
Abstract
Background Nut consumption has been associated with a reduced risk of coronary heart disease (CHD). In a recently completed clinical study we found that nut consumption significantly improved glycemic control and blood lipid risk factors for CHD compared to a healthy control. Objectives To determine if tree nuts improve other markers of cardiovascular risk including serum fatty acid profile, LDL particle sixe, clotting factors and markers of oxidative stress. Methods 117 subjects with type 2 diabetes were randomized to a 3‐month parallel design study. Subjects were randomized to one of three treatments: 1) Test (Full Dose Nut Diet): 75g/d for 2,000kcal/d; 2) Test (Half Dose Nut Diet): half‐dose of nuts and half‐dose of control muffin; and 3) Control: whole wheat muffins matched with energy content of nut supplements. Fasting blood samples were collected at baseline and weeks 2, 4, 8, 10 and 12 for markers of glycemic control and CHD risk factors. Results Compared to the control, the full dose nut supplement significantly lowered HbA1c (P=0.039), total‐C (P=0.002), LDL‐C (P=0.007), total‐C:HDL‐C (P=0.015), and LDL‐C:HDL‐C (P=0.028). Data on other markers of cardiovascular risk will 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".