Nuts as a Replacement for Carbohydrates in the Diabetic Diet
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
OBJECTIVE: Fat intake, especially monounsaturated fatty acid (MUFA), has been liberalized in diabetic diets to preserve HDL cholesterol and improve glycemic control, yet the exact sources have not been clearly defined. Therefore, we assessed the effect of mixed nut consumption as a source of vegetable fat on serum lipids and HbA(1c) in type 2 diabetes. RESEARCH DESIGN AND METHODS: A total of 117 type 2 diabetic subjects were randomized to one of three treatments for 3 months. Supplements were provided at 475 kcal per 2,000-kcal diet as mixed nuts (75 g/day), muffins, or half portions of both. The primary outcome was change in HbA(1c). RESULTS: The relative increase in MUFAs was 8.7% energy on the full-nut dose compared with muffins. Using an intention-to-treat analysis (n = 117), full-nut dose (mean intake 73 g/day) reduced HbA(1c) (-0.21% absolute HbA(1c) units, 95% CI -0.30 to -0.11, P < 0.001) with no change after half-nut dose or muffin. Full-nut dose was significantly different from half-nut dose (P = 0.004) and muffin (P = 0.001), but no difference was seen between half-nut dose and muffins. LDL cholesterol also decreased significantly after full-nut dose compared with muffin. The LDL cholesterol reduction after half-nut dose was intermediate and not significantly different from the other treatments. Apolipoprotein (apo) B and the apoB:apoA1 ratio behaved similarly. Nut intake related negatively to changes in HbA(1c) (r = -0.20, P = 0.033) and LDL cholesterol (r = -0.24, P = 0.011). CONCLUSIONS: Two ounces of nuts daily as a replacement for carbohydrate foods improved both glycemic control and serum lipids in type 2 diabetes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 |
| 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".